Data Mining - Computer Assignment 1

Mohammad Saadati - 810198410

Introduction

The goal of this computer assignment is review the data set of COVID-19, pre-processing, illustration and express our analysis based on the illustrations performed. This data set have information about patients, new tests, patient deaths and some features related to countries are given separately by day.

Import Libraries

First of all, we import necessary libraries to use their functions.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import time
import math
import jdatetime
import datetime
import seaborn as sns
import datetime as dt
import plotly.express as px
import geopandas

Preprocessing

Pre-processing is one of the most important steps in data mining projects. Various approaches are used in the field of lost data management and data conversion to other formats, and the careful selection of these approaches has a direct impact on the quality of the final results; Therefore, the best approach should always be identified and applied.

First we load csv file as a DataFrame using pandas library.

df = pd.read_csv("CA1_Dataset.csv")
df

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
0	AFG	Asia	Afghanistan	2020-02-24	5.0	5.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
1	AFG	Asia	Afghanistan	2020-02-25	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
2	AFG	Asia	Afghanistan	2020-02-26	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
3	AFG	Asia	Afghanistan	2020-02-27	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
4	AFG	Asia	Afghanistan	2020-02-28	5.0	0.0	NaN	NaN	NaN	NaN	...	NaN	NaN	37.746	0.5	64.83	0.511	NaN	NaN	NaN	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
165631	ZWE	Africa	Zimbabwe	2022-02-26	235803.0	336.0	368.429	5393.0	1.0	1.000	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
165632	ZWE	Africa	Zimbabwe	2022-02-27	235803.0	0.0	350.143	5393.0	0.0	1.000	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
165633	ZWE	Africa	Zimbabwe	2022-02-28	236380.0	577.0	401.286	5395.0	2.0	1.286	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
165634	ZWE	Africa	Zimbabwe	2022-03-01	236871.0	491.0	413.000	5395.0	0.0	1.000	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN
165635	ZWE	Africa	Zimbabwe	2022-03-02	237503.0	632.0	416.286	5396.0	1.0	1.143	...	1.6	30.7	36.791	1.7	61.49	0.571	NaN	NaN	NaN	NaN

165636 rows × 67 columns

Question 1

In this part first we use two Pandas function (isna and sum) to count number of missing values at each column.

df.info()
pd.set_option('display.max_rows',None)
df.isna().sum()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 165636 entries, 0 to 165635
Data columns (total 67 columns):
 #   Column                                      Non-Null Count   Dtype  
---  ------                                      --------------   -----  
 0   iso_code                                    165636 non-null  object 
 1   continent                                   155719 non-null  object 
 2   location                                    165636 non-null  object 
 3   date                                        165636 non-null  object 
 4   total_cases                                 162606 non-null  float64
 5   new_cases                                   162464 non-null  float64
 6   new_cases_smoothed                          160480 non-null  float64
 7   total_deaths                                144793 non-null  float64
 8   new_deaths                                  144833 non-null  float64
 9   new_deaths_smoothed                         142734 non-null  float64
 10  total_cases_per_million                     161851 non-null  float64
 11  new_cases_per_million                       161709 non-null  float64
 12  new_cases_smoothed_per_million              159731 non-null  float64
 13  total_deaths_per_million                    144051 non-null  float64
 14  new_deaths_per_million                      144091 non-null  float64
 15  new_deaths_smoothed_per_million             141998 non-null  float64
 16  reproduction_rate                           125067 non-null  float64
 17  icu_patients                                23390 non-null   float64
 18  icu_patients_per_million                    23390 non-null   float64
 19  hosp_patients                               24564 non-null   float64
 20  hosp_patients_per_million                   24564 non-null   float64
 21  weekly_icu_admissions                       5404 non-null    float64
 22  weekly_icu_admissions_per_million           5404 non-null    float64
 23  weekly_hosp_admissions                      10877 non-null   float64
 24  weekly_hosp_admissions_per_million          10877 non-null   float64
 25  new_tests                                   67006 non-null   float64
 26  total_tests                                 68944 non-null   float64
 27  total_tests_per_thousand                    68944 non-null   float64
 28  new_tests_per_thousand                      67006 non-null   float64
 29  new_tests_smoothed                          83658 non-null   float64
 30  new_tests_smoothed_per_thousand             83658 non-null   float64
 31  positive_rate                               78590 non-null   float64
 32  tests_per_case                              78027 non-null   float64
 33  tests_units                                 85981 non-null   object 
 34  total_vaccinations                          44978 non-null   float64
 35  people_vaccinated                           42792 non-null   float64
 36  people_fully_vaccinated                     40028 non-null   float64
 37  total_boosters                              17340 non-null   float64
 38  new_vaccinations                            37252 non-null   float64
 39  new_vaccinations_smoothed                   84112 non-null   float64
 40  total_vaccinations_per_hundred              44978 non-null   float64
 41  people_vaccinated_per_hundred               42792 non-null   float64
 42  people_fully_vaccinated_per_hundred         40028 non-null   float64
 43  total_boosters_per_hundred                  17340 non-null   float64
 44  new_vaccinations_smoothed_per_million       84112 non-null   float64
 45  new_people_vaccinated_smoothed              82821 non-null   float64
 46  new_people_vaccinated_smoothed_per_hundred  82821 non-null   float64
 47  stringency_index                            129862 non-null  float64
 48  population                                  164564 non-null  float64
 49  population_density                          147313 non-null  float64
 50  median_age                                  137258 non-null  float64
 51  aged_65_older                               135770 non-null  float64
 52  aged_70_older                               136522 non-null  float64
 53  gdp_per_capita                              137928 non-null  float64
 54  extreme_poverty                             90837 non-null   float64
 55  cardiovasc_death_rate                       136208 non-null  float64
 56  diabetes_prevalence                         143349 non-null  float64
 57  female_smokers                              105609 non-null  float64
 58  male_smokers                                104160 non-null  float64
 59  handwashing_facilities                      68284 non-null   float64
 60  hospital_beds_per_thousand                  123151 non-null  float64
 61  life_expectancy                             154620 non-null  float64
 62  human_development_index                     135683 non-null  float64
 63  excess_mortality_cumulative_absolute        5696 non-null    float64
 64  excess_mortality_cumulative                 5696 non-null    float64
 65  excess_mortality                            5696 non-null    float64
 66  excess_mortality_cumulative_per_million     5696 non-null    float64
dtypes: float64(62), object(5)
memory usage: 84.7+ MB

iso_code                                           0
continent                                       9917
location                                           0
date                                               0
total_cases                                     3030
new_cases                                       3172
new_cases_smoothed                              5156
total_deaths                                   20843
new_deaths                                     20803
new_deaths_smoothed                            22902
total_cases_per_million                         3785
new_cases_per_million                           3927
new_cases_smoothed_per_million                  5905
total_deaths_per_million                       21585
new_deaths_per_million                         21545
new_deaths_smoothed_per_million                23638
reproduction_rate                              40569
icu_patients                                  142246
icu_patients_per_million                      142246
hosp_patients                                 141072
hosp_patients_per_million                     141072
weekly_icu_admissions                         160232
weekly_icu_admissions_per_million             160232
weekly_hosp_admissions                        154759
weekly_hosp_admissions_per_million            154759
new_tests                                      98630
total_tests                                    96692
total_tests_per_thousand                       96692
new_tests_per_thousand                         98630
new_tests_smoothed                             81978
new_tests_smoothed_per_thousand                81978
positive_rate                                  87046
tests_per_case                                 87609
tests_units                                    79655
total_vaccinations                            120658
people_vaccinated                             122844
people_fully_vaccinated                       125608
total_boosters                                148296
new_vaccinations                              128384
new_vaccinations_smoothed                      81524
total_vaccinations_per_hundred                120658
people_vaccinated_per_hundred                 122844
people_fully_vaccinated_per_hundred           125608
total_boosters_per_hundred                    148296
new_vaccinations_smoothed_per_million          81524
new_people_vaccinated_smoothed                 82815
new_people_vaccinated_smoothed_per_hundred     82815
stringency_index                               35774
population                                      1072
population_density                             18323
median_age                                     28378
aged_65_older                                  29866
aged_70_older                                  29114
gdp_per_capita                                 27708
extreme_poverty                                74799
cardiovasc_death_rate                          29428
diabetes_prevalence                            22287
female_smokers                                 60027
male_smokers                                   61476
handwashing_facilities                         97352
hospital_beds_per_thousand                     42485
life_expectancy                                11016
human_development_index                        29953
excess_mortality_cumulative_absolute          159940
excess_mortality_cumulative                   159940
excess_mortality                              159940
excess_mortality_cumulative_per_million       159940
dtype: int64

Now use fillna function to fill out the missing values in the given column.

To fill in the missing values in numeric columns:

• If the column is total or new, set the value to zero.
• If the percentage of missing values is more than 90, we will fill it with infinite negatives.
• Otherwise, we fill in the missing values with the average value of other data.

To fill in the missing values in the continent column:

• If the location value corresponding to that row was equal to the name of a continent, we fill that row with the location value.
• Otherwise we set the Undefined value for the missing values.

To fill in the missing values in the tests_units column:

• We filled in the missing values as the mode values of the other data

Note: for tests_units column, at first we tried to fill in the missing values with the corresponding location mode, but since it took a long time, we did not do this method.

unique_continent = df['continent'].unique()

for column in df:
    if df[column].isna().sum() > 0:
        if df[column].dtypes != object:
            if column[0] == 't' or column[0] == 'n':
                df[column].fillna(0, inplace=True)
            elif df[column].isna().sum() / df.shape[0] > 0.9:
                df[column].fillna(-np.inf, inplace=True)
            else:
                df[column].fillna(df[column].mean(), inplace=True)
        elif column == "continent":
            df['continent'] = df.apply(lambda row: row['location'] if (pd.isna(row['continent']) and (row['location'] in unique_continent)) 
                                       else ('Undefined' if pd.isna(row['continent']) else row['continent']) , axis=1)
        elif column == "tests_units":
            df['tests_units'].fillna(df['tests_units'].mode()[0], inplace=True) 
#             df['tests_units'] = df.apply( lambda row : df['tests_units'].mode()[0] if (df[df['location'] == row['location']]['tests_units'].isnull().values.all())
#                                         else (df[df['location'] == row['location']]['tests_units'].mode()[0] if pd.isna(row['tests_units'])
#                                         else row['tests_units']), axis = 1)

#df.to_csv("df_p1_q1.csv")
df.isna().sum()

iso_code                                      0
continent                                     0
location                                      0
date                                          0
total_cases                                   0
new_cases                                     0
new_cases_smoothed                            0
total_deaths                                  0
new_deaths                                    0
new_deaths_smoothed                           0
total_cases_per_million                       0
new_cases_per_million                         0
new_cases_smoothed_per_million                0
total_deaths_per_million                      0
new_deaths_per_million                        0
new_deaths_smoothed_per_million               0
reproduction_rate                             0
icu_patients                                  0
icu_patients_per_million                      0
hosp_patients                                 0
hosp_patients_per_million                     0
weekly_icu_admissions                         0
weekly_icu_admissions_per_million             0
weekly_hosp_admissions                        0
weekly_hosp_admissions_per_million            0
new_tests                                     0
total_tests                                   0
total_tests_per_thousand                      0
new_tests_per_thousand                        0
new_tests_smoothed                            0
new_tests_smoothed_per_thousand               0
positive_rate                                 0
tests_per_case                                0
tests_units                                   0
total_vaccinations                            0
people_vaccinated                             0
people_fully_vaccinated                       0
total_boosters                                0
new_vaccinations                              0
new_vaccinations_smoothed                     0
total_vaccinations_per_hundred                0
people_vaccinated_per_hundred                 0
people_fully_vaccinated_per_hundred           0
total_boosters_per_hundred                    0
new_vaccinations_smoothed_per_million         0
new_people_vaccinated_smoothed                0
new_people_vaccinated_smoothed_per_hundred    0
stringency_index                              0
population                                    0
population_density                            0
median_age                                    0
aged_65_older                                 0
aged_70_older                                 0
gdp_per_capita                                0
extreme_poverty                               0
cardiovasc_death_rate                         0
diabetes_prevalence                           0
female_smokers                                0
male_smokers                                  0
handwashing_facilities                        0
hospital_beds_per_thousand                    0
life_expectancy                               0
human_development_index                       0
excess_mortality_cumulative_absolute          0
excess_mortality_cumulative                   0
excess_mortality                              0
excess_mortality_cumulative_per_million       0
dtype: int64

Advantages and disadvantages of using mean for missing values:

Missing values in your data do not reduce your sample size, as it would be the case with listwise deletion (the default of many statistical software packages, e.g. R, Stata, SAS or SPSS). Since mean imputation replaces all missing values, you can keep your whole database.

Replacing missing data by the mean of nonmissing data causes the population SD to be underestimated, but may also obscure important features of the population from which the data were sampled. Another possible disadvantage with using the mean for missing values is that the reason the values are missing in the first place could be dependent on the missing values themselves. (This is called missing not at random.)

Advantages : Easy to apply - Mean will not change

Disadvantages : Results may not be accurate - For large amount of NaN data this method can increase mod value significantly and cause error in results - The variance will decrease

Question 2

In this section, in a new dataframe called df_p1_q2, we calculate the number of new_cases, the number of new_vaccinations, the number of new_deaths and the population for each country aggregated.

We use the groupby function to aggregate. We use sum() method to aggregate the new columns and max() method to aggregate the total columns

pd.reset_option('display.max_rows')

df_p1_q2 = df[["location", "new_cases", "new_vaccinations", "new_deaths"]]
df_p1_q2 = df_p1_q2.groupby(by="location").sum()

df_p1_q2_p = df[["location", "population"]]
df_p1_q2_p = df_p1_q2_p.groupby(by="location").max()

df_p1_q2 = df_p1_q2.join(df_p1_q2_p)
df_p1_q2

	new_cases	new_vaccinations	new_deaths	population
location
Afghanistan	174081.0	1.374200e+04	7617.0	3.983543e+07
Africa	11230524.0	5.818190e+08	248668.0	1.373486e+09
Albania	271825.0	1.415150e+06	3474.0	2.872934e+06
Algeria	265079.0	1.707860e+05	6843.0	4.461663e+07
Andorra	38249.0	4.802000e+03	151.0	7.735400e+04
...	...	...	...	...
Wallis and Futuna	454.0	0.000000e+00	7.0	1.109400e+04
World	439011701.0	1.122516e+10	5946817.0	7.874966e+09
Yemen	11772.0	0.000000e+00	2135.0	3.049064e+07
Zambia	313203.0	1.321156e+06	3955.0	1.892066e+07
Zimbabwe	237509.0	7.080893e+06	5396.0	1.509217e+07

238 rows × 4 columns

Question 3

In this part we use jdatetime.date.fromgregorian function from jdatetime library to convert gregorian date to shamsi date and store the result in shamsi_date column of a new dataframe called df_p1_q3.

def convert_gregorian_date_to_shamsi_date(gregorian_date):
    y, m , d = gregorian_date.split("-")
    return str(jdatetime.date.fromgregorian(day=int(d),month=int(m),year=int(y)))

df['shamsi_date'] = df['date']

for index, row in df.iterrows():
    shamsi_date = convert_gregorian_date_to_shamsi_date(row['shamsi_date'])
    df.at[index,'shamsi_date'] = shamsi_date

#df.to_csv("df_p1_q3.csv")
df

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million	shamsi_date
0	AFG	Asia	Afghanistan	2020-02-24	5.0	5.0	0.000	0.0	0.0	0.000	...	32.778255	37.746	0.5	64.83	0.511	-inf	-inf	-inf	-inf	1398-12-05
1	AFG	Asia	Afghanistan	2020-02-25	5.0	0.0	0.000	0.0	0.0	0.000	...	32.778255	37.746	0.5	64.83	0.511	-inf	-inf	-inf	-inf	1398-12-06
2	AFG	Asia	Afghanistan	2020-02-26	5.0	0.0	0.000	0.0	0.0	0.000	...	32.778255	37.746	0.5	64.83	0.511	-inf	-inf	-inf	-inf	1398-12-07
3	AFG	Asia	Afghanistan	2020-02-27	5.0	0.0	0.000	0.0	0.0	0.000	...	32.778255	37.746	0.5	64.83	0.511	-inf	-inf	-inf	-inf	1398-12-08
4	AFG	Asia	Afghanistan	2020-02-28	5.0	0.0	0.000	0.0	0.0	0.000	...	32.778255	37.746	0.5	64.83	0.511	-inf	-inf	-inf	-inf	1398-12-09
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
165631	ZWE	Africa	Zimbabwe	2022-02-26	235803.0	336.0	368.429	5393.0	1.0	1.000	...	30.700000	36.791	1.7	61.49	0.571	-inf	-inf	-inf	-inf	1400-12-07
165632	ZWE	Africa	Zimbabwe	2022-02-27	235803.0	0.0	350.143	5393.0	0.0	1.000	...	30.700000	36.791	1.7	61.49	0.571	-inf	-inf	-inf	-inf	1400-12-08
165633	ZWE	Africa	Zimbabwe	2022-02-28	236380.0	577.0	401.286	5395.0	2.0	1.286	...	30.700000	36.791	1.7	61.49	0.571	-inf	-inf	-inf	-inf	1400-12-09
165634	ZWE	Africa	Zimbabwe	2022-03-01	236871.0	491.0	413.000	5395.0	0.0	1.000	...	30.700000	36.791	1.7	61.49	0.571	-inf	-inf	-inf	-inf	1400-12-10
165635	ZWE	Africa	Zimbabwe	2022-03-02	237503.0	632.0	416.286	5396.0	1.0	1.143	...	30.700000	36.791	1.7	61.49	0.571	-inf	-inf	-inf	-inf	1400-12-11

165636 rows × 68 columns

Question 4

Yes, for example, you can ignore the total column and replace the aggregate values of the new columns with the total column.

Redundant attributes may be able to be detected by correlation analysis and covariance analysis

df.corr()

	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	total_cases_per_million	new_cases_per_million	new_cases_smoothed_per_million	total_deaths_per_million	...	female_smokers	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million
total_cases	1.000000	0.806677	0.827899	0.968956	0.726685	0.747316	0.104767	0.032705	0.043368	0.137671	...	-0.002716	-0.005809	0.025074	-0.004761	0.010728	0.029702	0.802190	0.139572	0.034064	0.228241
new_cases	0.806677	1.000000	0.976958	0.735240	0.668096	0.654269	0.097389	0.088765	0.098912	0.103075	...	0.002898	-0.001433	0.020072	0.002557	0.012515	0.028335	0.464078	0.090745	0.092382	0.124742
new_cases_smoothed	0.827899	0.976958	1.000000	0.749612	0.663865	0.673995	0.101359	0.078399	0.101462	0.105567	...	0.002698	-0.001564	0.020375	0.002142	0.012423	0.028472	0.509117	0.075321	0.070259	0.127365
total_deaths	0.968956	0.735240	0.749612	1.000000	0.781224	0.803281	0.085529	0.017392	0.022912	0.156600	...	-0.006089	-0.006023	0.030088	-0.006634	0.010865	0.030726	0.928284	0.338096	0.110488	0.370520
new_deaths	0.726685	0.668096	0.663865	0.781224	1.000000	0.975575	0.031455	0.016238	0.017744	0.085165	...	-0.006418	-0.000462	0.032785	-0.003287	0.009868	0.032106	0.493040	0.301056	0.395089	0.193819
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
human_development_index	0.029702	0.028335	0.028472	0.030726	0.032106	0.032645	0.343099	0.158589	0.199781	0.326067	...	0.483810	0.082491	0.447246	0.468142	0.847106	1.000000	-0.102242	-0.347135	-0.290497	-0.231463
excess_mortality_cumulative_absolute	0.802190	0.464078	0.509117	0.928284	0.493040	0.655323	0.169916	0.008444	0.018781	0.376935	...	-0.122511	-0.115914	0.102151	-0.168047	-0.195392	-0.102242	1.000000	0.397512	0.167042	0.445995
excess_mortality_cumulative	0.139572	0.090745	0.075321	0.338096	0.301056	0.254753	0.232222	0.057043	0.041999	0.651316	...	-0.261507	-0.066339	0.203881	-0.252829	-0.253115	-0.347135	0.397512	1.000000	0.551706	0.738794
excess_mortality	0.034064	0.092382	0.070259	0.110488	0.395089	0.322358	0.100555	0.118010	0.117073	0.264369	...	-0.119547	0.052966	0.145492	-0.111678	-0.257628	-0.290497	0.167042	0.551706	1.000000	0.382483
excess_mortality_cumulative_per_million	0.228241	0.124742	0.127365	0.370520	0.193819	0.203090	0.553645	0.162462	0.182880	0.863743	...	0.020326	0.117764	0.097386	0.007885	-0.279109	-0.231463	0.445995	0.738794	0.382483	1.000000

62 rows × 62 columns

Question 5

Creating a new dataframe that has only information about Iran and we called it df_iran.

df_iran = df[df["iso_code"] == "IRN"]
df_iran

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million	shamsi_date
71639	IRN	Asia	Iran	2020-02-19	2.0	2.0	0.000	2.0	2.0	0.000	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-11-30
71640	IRN	Asia	Iran	2020-02-20	5.0	3.0	0.000	2.0	0.0	0.000	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-12-01
71641	IRN	Asia	Iran	2020-02-21	18.0	13.0	0.000	4.0	2.0	0.000	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-12-02
71642	IRN	Asia	Iran	2020-02-22	28.0	10.0	0.000	5.0	1.0	0.000	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-12-03
71643	IRN	Asia	Iran	2020-02-23	43.0	15.0	0.000	8.0	3.0	0.000	...	21.1	50.790872	1.5	76.68	0.783	-2658.0	-4.14	2.79	-31.260011	1398-12-04
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
72377	IRN	Asia	Iran	2022-02-26	7030943.0	7039.0	15065.429	136390.0	224.0	227.429	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-07
72378	IRN	Asia	Iran	2022-02-27	7040467.0	9524.0	14002.143	136631.0	241.0	227.286	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-08
72379	IRN	Asia	Iran	2022-02-28	7051429.0	10962.0	12838.143	136838.0	207.0	223.143	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-09
72380	IRN	Asia	Iran	2022-03-01	7060741.0	9312.0	11015.143	137064.0	226.0	223.571	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-10
72381	IRN	Asia	Iran	2022-03-02	7066975.0	6234.0	9714.286	137267.0	203.0	220.143	...	21.1	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-11

743 rows × 68 columns

Question 6

In this part, we will create a new column called shamsi_month for df_iran, which is considered as an independent feature in that month.

df_iran['shamsi_month'] = df_iran["shamsi_date"]

for index, row in df_iran.iterrows():
    y, m , d = row['shamsi_date'].split("-")
    df_iran.at[index,'shamsi_month'] = m

df_iran

/tmp/ipykernel_4500/4173350697.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_iran['shamsi_month'] = df_iran["shamsi_date"]

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million	shamsi_date	shamsi_month
71639	IRN	Asia	Iran	2020-02-19	2.0	2.0	0.000	2.0	2.0	0.000	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-11-30	11
71640	IRN	Asia	Iran	2020-02-20	5.0	3.0	0.000	2.0	0.0	0.000	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-12-01	12
71641	IRN	Asia	Iran	2020-02-21	18.0	13.0	0.000	4.0	2.0	0.000	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-12-02	12
71642	IRN	Asia	Iran	2020-02-22	28.0	10.0	0.000	5.0	1.0	0.000	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1398-12-03	12
71643	IRN	Asia	Iran	2020-02-23	43.0	15.0	0.000	8.0	3.0	0.000	...	50.790872	1.5	76.68	0.783	-2658.0	-4.14	2.79	-31.260011	1398-12-04	12
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
72377	IRN	Asia	Iran	2022-02-26	7030943.0	7039.0	15065.429	136390.0	224.0	227.429	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-07	12
72378	IRN	Asia	Iran	2022-02-27	7040467.0	9524.0	14002.143	136631.0	241.0	227.286	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-08	12
72379	IRN	Asia	Iran	2022-02-28	7051429.0	10962.0	12838.143	136838.0	207.0	223.143	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-09	12
72380	IRN	Asia	Iran	2022-03-01	7060741.0	9312.0	11015.143	137064.0	226.0	223.571	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-10	12
72381	IRN	Asia	Iran	2022-03-02	7066975.0	6234.0	9714.286	137267.0	203.0	220.143	...	50.790872	1.5	76.68	0.783	-inf	-inf	-inf	-inf	1400-12-11	12

743 rows × 69 columns

Question 7

In this part, we create a new dataframe that aggregates the Iranian dataframe by month in 2021 and called it df_p1_q7.

We use the groupby function to aggregate. We use sum() method to aggregate the new columns and max() method to aggregate the total columns

new_column = []
for column in df_iran:
    if column[0] == 'n':
        new_column.append(column)
new_column.append('shamsi_month')
        
df_iran_new_column = df_iran[new_column].copy()
new_column.pop()
df_iran_without_new_column = df_iran.copy()
df_iran_without_new_column.drop(new_column, axis = 1, inplace = True)
    
df_iran_without_new_column = df_iran_without_new_column.groupby(by="shamsi_month").max()
df_iran_new_column = df_iran_new_column.groupby(by="shamsi_month").sum()

df_p1_q7 = df_iran_without_new_column.join(df_iran_new_column)
#df_p1_q7.to_csv("df_p1_q7.csv")
df_p1_q7

	iso_code	continent	location	date	total_cases	total_deaths	total_cases_per_million	total_deaths_per_million	reproduction_rate	icu_patients	...	new_deaths_smoothed_per_million	new_tests	new_tests_per_thousand	new_tests_smoothed	new_tests_smoothed_per_thousand	new_vaccinations	new_vaccinations_smoothed	new_vaccinations_smoothed_per_million	new_people_vaccinated_smoothed	new_people_vaccinated_smoothed_per_hundred
shamsi_month
01	IRN	Asia	Iran	2021-04-20	2286927.0	67525.0	26895.923	794.143	1.81	903.707952	...	104.570	730425.0	8.592	2467835.0	29.022	71691.0	403349.0	4741.0	284774.0	0.339
02	IRN	Asia	Iran	2021-05-21	2815882.0	78194.0	33116.818	919.618	1.27	903.707952	...	155.468	409622.0	4.817	4373639.0	51.435	129369.0	1937861.0	22790.0	1701185.0	2.002
03	IRN	Asia	Iran	2021-06-21	3105620.0	83101.0	36524.348	977.328	1.30	903.707952	...	87.378	658679.0	7.744	4542917.0	53.426	0.0	2432576.0	28607.0	1897683.0	2.227
04	IRN	Asia	Iran	2021-07-22	3623840.0	88063.0	42618.992	1035.685	1.38	903.707952	...	111.742	777132.0	9.138	3724340.0	43.798	0.0	3276433.0	38530.0	1884147.0	2.213
05	IRN	Asia	Iran	2021-08-22	4677114.0	102038.0	55006.259	1200.041	1.28	903.707952	...	221.069	1245457.0	14.647	3250257.0	38.228	0.0	12940963.0	152194.0	10180866.0	11.972
06	IRN	Asia	Iran	2021-09-22	5477229.0	118191.0	64416.193	1390.012	1.26	903.707952	...	246.029	3072546.0	36.135	4207928.0	49.488	2587215.0	21652513.0	254648.0	12837489.0	15.097
07	IRN	Asia	Iran	2021-10-22	5844589.0	124928.0	68736.613	1469.244	1.26	903.707952	...	161.503	2017449.0	23.725	4040342.0	47.517	0.0	32477191.0	381956.0	20348580.0	23.934
08	IRN	Asia	Iran	2021-11-21	6077438.0	128956.0	71475.087	1516.616	1.32	903.707952	...	190.918	2230156.0	26.230	4291678.0	50.474	0.0	25418375.0	298939.0	6997855.0	8.231
09	IRN	Asia	Iran	2021-12-21	6175782.0	131167.0	72631.684	1542.619	1.12	903.707952	...	152.994	2279653.0	26.809	4520663.0	53.166	171719.0	12062583.0	141866.0	2785809.0	3.280
10	IRN	Asia	Iran	2022-01-20	6236567.0	132152.0	73346.559	1554.204	1.72	903.707952	...	55.381	2630337.0	30.933	4428291.0	52.079	2717110.0	13690288.0	161008.0	1314004.0	1.543
11	IRN	Asia	Iran	2022-02-19	6925485.0	134798.0	81448.736	1585.322	2.38	903.707952	...	52.618	1145796.0	13.473	4433371.0	52.143	8683000.0	12010588.0	141250.0	1461538.0	1.719
12	IRN	Asia	Iran	2022-03-02	7066975.0	137267.0	83112.761	1614.360	4.92	903.707952	...	66.977	398263.0	4.684	2512695.0	29.553	2375342.0	2955310.0	34762.0	1089561.0	1.270

12 rows × 68 columns

Display data

One of the most used items in data mining is data visualization, which can be used to gain an understanding of the data set and also provide a complete analysis based on the obtained diagrams.

plt.rcParams["font.family"] = "serif"

Question 1

In this section, using a bar chart, we identify the countries that have had the best and worst performance (number of deaths relative to the total population) in controlling the corona virus.

df_p2_q1 = df_p1_q2.copy()
df_p2_q1['deaths_per_pop'] = df_p1_q2['new_deaths'] / df_p1_q2['population']
df_p2_q1 = df_p2_q1.sort_values(by= ['deaths_per_pop'], ascending= [False])

plt.figure(figsize=(200,40))
ax = sns.barplot(df_p2_q1.index, df_p2_q1['deaths_per_pop'] , alpha=0.8, color='r')
ax.set_title("Performance in Inhibiting Corona Virus",fontsize=25)
ax.set_xlabel('Location',fontsize=25)
ax.set_ylabel('Performance',fontsize=25)
ax.set_xticklabels(df_p2_q1.index, rotation='vertical', fontsize=25)
plt.xticks(rotation = 80)
plt.show()

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

• The Peru, Bulgaria, Bosnia and Herzegovina, Hungary and North Macedonia countries have had the best performance in controlling the corona virus.

• The Jersey ,Samoa ,Falkland Islands, Guernsey and Tonga countries have had the worst performance in controlling the corona virus.

Question 2

In this part, we want to investigate the effect of vaccination on the number of deaths. To do this, for each country, we calculate the number of new_vaccinations per population of that country, and after sorting the obtained values, we select one country out of every fifth one for review and comparison.

pd.set_option('display.max_rows',None)

df_p2_q2= df_p1_q2.copy()
df_p2_q2['ratio1'] = (df_p2_q2['new_vaccinations'] / df_p2_q2['population']) 
df_p2_q2['ratio2'] = (df_p2_q2['new_deaths'] / df_p2_q2['population']) 

df_p2_q2 = df_p2_q2.sort_values(by=['ratio1'], ascending= [False])

df_p2_q2

	new_cases	new_vaccinations	new_deaths	population	ratio1	ratio2
location
Cuba	1070730.0	3.065243e+07	8497.0	1.131750e+07	2.708410	7.507843e-04
Gibraltar	15632.0	8.906400e+04	101.0	3.369100e+04	2.643555	2.997833e-03
Chile	3098110.0	4.855777e+07	42624.0	1.921236e+07	2.527423	2.218572e-03
Singapore	767663.0	1.357254e+07	1040.0	5.453600e+06	2.488730	1.906997e-04
Malta	71497.0	1.205547e+06	608.0	5.161000e+05	2.335879	1.178066e-03
South Korea	3691487.0	1.192514e+08	8394.0	5.130518e+07	2.324354	1.636092e-04
Denmark	2805858.0	1.317204e+07	4633.0	5.813302e+06	2.265845	7.969653e-04
Italy	12868066.0	1.343115e+08	155245.0	6.036747e+07	2.224899	2.571666e-03
Uruguay	846889.0	7.622054e+06	7005.0	3.485152e+06	2.187008	2.009955e-03
Belgium	3563841.0	2.504046e+07	30316.0	1.163233e+07	2.152660	2.606184e-03
Canada	3303284.0	8.106477e+07	36728.0	3.806791e+07	2.129478	9.648020e-04
Ireland	1315500.0	1.056764e+07	6540.0	4.982904e+06	2.120779	1.312488e-03
China	109423.0	3.061013e+09	4640.0	1.444216e+09	2.119498	3.212816e-06
France	23316777.0	1.408496e+08	138537.0	6.742200e+07	2.089075	2.054774e-03
Australia	3326214.0	5.379783e+07	5319.0	2.578822e+07	2.086140	2.062570e-04
New Zealand	166103.0	1.067335e+07	56.0	5.126300e+06	2.082077	1.092406e-05
Malaysia	3496090.0	6.741625e+07	32942.0	3.277620e+07	2.056866	1.005059e-03
Norway	1279169.0	1.123317e+07	1666.0	5.465629e+06	2.055238	3.048140e-04
Cambodia	131004.0	3.467897e+07	3032.0	1.694645e+07	2.046386	1.789166e-04
Germany	15175471.0	1.701530e+08	123431.0	8.390047e+07	2.028034	1.471160e-03
United Arab Emirates	880970.0	2.018434e+07	2301.0	9.991083e+06	2.020235	2.303054e-04
United Kingdom	18241147.0	1.372498e+08	160727.0	6.820711e+07	2.012251	2.356455e-03
Israel	3652454.0	1.801561e+07	10245.0	9.291000e+06	1.939039	1.102680e-03
Portugal	3282618.0	1.964667e+07	21111.0	1.016792e+07	1.932221	2.076235e-03
Upper middle income	118212049.0	4.804681e+09	2421471.0	2.513673e+09	1.911419	9.633199e-04
High income	236393139.0	2.306054e+09	2211971.0	1.214930e+09	1.898096	1.820657e-03
Luxembourg	186210.0	1.178840e+06	995.0	6.348140e+05	1.856985	1.567388e-03
Greece	2454429.0	1.907120e+07	25972.0	1.037075e+07	1.838941	2.504352e-03
Liechtenstein	12165.0	7.022800e+04	78.0	3.825400e+04	1.835834	2.039002e-03
Peru	3518721.0	6.113136e+07	211453.0	3.335942e+07	1.832507	6.338630e-03
European Union	110033392.0	8.184271e+08	1015772.0	4.471899e+08	1.830156	2.271456e-03
Brazil	28741413.0	3.902730e+08	650254.0	2.139934e+08	1.823761	3.038663e-03
Argentina	8912317.0	8.251164e+07	126390.0	4.560582e+07	1.809235	2.771357e-03
Switzerland	2842412.0	1.556814e+07	13269.0	8.715494e+06	1.786260	1.522461e-03
Ecuador	844764.0	3.131789e+07	22611.0	1.788847e+07	1.750730	1.263998e-03
Qatar	357583.0	5.106175e+06	670.0	2.930524e+06	1.742410	2.286281e-04
North America	93195128.0	1.030269e+09	1374621.0	5.965813e+08	1.726955	2.304164e-03
Oceania	3735739.0	7.462766e+07	7922.0	4.321995e+07	1.726695	1.832950e-04
Hong Kong	293754.0	1.302273e+07	1168.0	7.552800e+06	1.724226	1.546446e-04
South America	54222621.0	7.462801e+08	1247285.0	4.342601e+08	1.718509	2.872207e-03
Turkey	13382896.0	1.434464e+08	94837.0	8.504274e+07	1.686757	1.115169e-03
Bahrain	519584.0	2.927653e+06	1511.0	1.748295e+06	1.674576	8.642706e-04
Europe	158815550.0	1.247823e+09	1717056.0	7.489630e+08	1.666068	2.292578e-03
United States	79143715.0	5.537498e+08	954518.0	3.329151e+08	1.663336	2.867152e-03
Lithuania	913223.0	4.449324e+06	8442.0	2.689862e+06	1.654109	3.138451e-03
Saudi Arabia	746066.0	5.737721e+07	9002.0	3.534068e+07	1.623546	2.547206e-04
Czechia	3602844.0	1.739288e+07	38771.0	1.072455e+07	1.621781	3.615162e-03
Asia	117811418.0	7.544342e+09	1351250.0	4.678445e+09	1.612575	2.888246e-04
Thailand	2958176.0	1.087844e+08	23072.0	6.995084e+07	1.555156	3.298316e-04
Taiwan	20584.0	3.695129e+07	853.0	2.385501e+07	1.548995	3.575769e-05
Latvia	673218.0	2.855540e+06	5284.0	1.866934e+06	1.529535	2.830309e-03
Sri Lanka	647699.0	3.284874e+07	16267.0	2.149731e+07	1.528040	7.566995e-04
Estonia	504148.0	1.955946e+06	2268.0	1.325188e+06	1.475976	1.711455e-03
World	439011701.0	1.122516e+10	5946817.0	7.874966e+09	1.425424	7.551547e-04
Slovenia	897040.0	2.956720e+06	6327.0	2.078723e+06	1.422373	3.043696e-03
Maldives	171114.0	7.707100e+05	297.0	5.436200e+05	1.417737	5.463375e-04
Panama	756085.0	5.934725e+06	8098.0	4.381583e+06	1.354471	1.848190e-03
Spain	11139724.0	6.310748e+07	100778.0	4.674521e+07	1.350031	2.155900e-03
Mongolia	909379.0	4.407760e+06	2171.0	3.329282e+06	1.323937	6.520926e-04
Japan	5143816.0	1.667069e+08	24140.0	1.260508e+08	1.322538	1.915101e-04
Vietnam	3709481.0	1.209025e+08	40637.0	9.816883e+07	1.231578	4.139501e-04
India	42945160.0	1.708969e+09	507015.0	1.393409e+09	1.226466	3.638666e-04
Dominican Republic	575157.0	1.331425e+07	4370.0	1.095371e+07	1.215501	3.989514e-04
Lower middle income	82588725.0	3.974325e+09	1271503.0	3.330653e+09	1.193257	3.817579e-04
Curacao	39016.0	1.840890e+05	261.0	1.647960e+05	1.117072	1.583776e-03
Mexico	5521744.0	1.451079e+08	311470.0	1.302622e+08	1.113968	2.391100e-03
Bhutan	13535.0	8.620330e+05	6.0	7.799000e+05	1.105312	7.693294e-06
Isle of Man	23099.0	9.206400e+04	80.0	8.541000e+04	1.077907	9.366585e-04
Trinidad and Tobago	128691.0	1.503347e+06	3637.0	1.403374e+06	1.071238	2.591611e-03
Bolivia	893775.0	1.258651e+07	21443.0	1.183294e+07	1.063684	1.812145e-03
Aruba	33684.0	1.091530e+05	212.0	1.071950e+05	1.018266	1.977704e-03
Macao	81.0	6.627460e+05	0.0	6.583910e+05	1.006615	0.000000e+00
Azerbaijan	787367.0	1.023182e+07	9454.0	1.022334e+07	1.000829	9.247463e-04
Barbados	55543.0	2.868630e+05	316.0	2.877080e+05	0.997063	1.098336e-03
Poland	5694767.0	3.742697e+07	111864.0	3.779700e+07	0.990210	2.959600e-03
Indonesia	5630096.0	2.724306e+08	149036.0	2.763618e+08	0.985775	5.392786e-04
Kazakhstan	1450652.0	1.870224e+07	19239.0	1.899496e+07	0.984590	1.012848e-03
Russia	16353868.0	1.434654e+08	346197.0	1.459120e+08	0.983232	2.372642e-03
Colombia	6067023.0	4.938413e+07	138899.0	5.126584e+07	0.963295	2.709387e-03
Montenegro	230512.0	5.963530e+05	2683.0	6.280510e+05	0.949530	4.271946e-03
Croatia	1058453.0	3.677004e+06	15122.0	4.081657e+06	0.900861	3.704868e-03
Romania	2748777.0	1.665058e+07	63668.0	1.912777e+07	0.870492	3.328563e-03
Hungary	1793120.0	8.276404e+06	44134.0	9.634162e+06	0.859068	4.580990e-03
Greenland	11760.0	4.333600e+04	19.0	5.686800e+04	0.762045	3.341071e-04
El Salvador	156456.0	4.886767e+06	4077.0	6.518500e+06	0.749677	6.254506e-04
Kosovo	226392.0	1.330147e+06	3116.0	1.782115e+06	0.746387	1.748484e-03
Guatemala	784024.0	1.341083e+07	17021.0	1.824987e+07	0.734845	9.326643e-04
Morocco	1161290.0	2.740605e+07	16002.0	3.734479e+07	0.733866	4.284935e-04
Ukraine	5040518.0	3.168315e+07	112459.0	4.346682e+07	0.728904	2.587238e-03
Tunisia	999441.0	8.530717e+06	27824.0	1.193576e+07	0.714719	2.331145e-03
Faeroe Islands	34658.0	3.481300e+04	28.0	4.905300e+04	0.709702	5.708112e-04
Lebanon	1072537.0	4.755512e+06	10115.0	6.769151e+06	0.702527	1.494279e-03
Iceland	135748.0	2.588510e+05	66.0	3.687920e+05	0.701889	1.789627e-04
Bulgaria	1096194.0	4.143716e+06	35696.0	6.896655e+06	0.600830	5.175842e-03
Brunei	71667.0	2.627530e+05	129.0	4.415320e+05	0.595094	2.921646e-04
San Marino	14435.0	2.014100e+04	112.0	3.401000e+04	0.592208	3.293149e-03
Suriname	78353.0	3.429360e+05	1317.0	5.917980e+05	0.579482	2.225422e-03
Paraguay	642573.0	4.105501e+06	18422.0	7.219641e+06	0.568657	2.551650e-03
Bangladesh	1945108.0	8.807564e+07	29053.0	1.663035e+08	0.529608	1.746987e-04
Philippines	3663931.0	5.494906e+07	56506.0	1.110469e+08	0.494827	5.088480e-04
Albania	271825.0	1.415150e+06	3474.0	2.872934e+06	0.492580	1.209217e-03
Cyprus	326611.0	4.282040e+05	859.0	8.960050e+05	0.477904	9.587000e-04
Serbia	1916707.0	3.257010e+06	15318.0	6.871547e+06	0.473985	2.229192e-03
Zimbabwe	237509.0	7.080893e+06	5396.0	1.509217e+07	0.469177	3.575364e-04
North Macedonia	298195.0	9.505510e+05	9036.0	2.082661e+06	0.456412	4.338680e-03
Uzbekistan	236596.0	1.512707e+07	1637.0	3.393576e+07	0.445756	4.823819e-05
Seychelles	39408.0	4.286300e+04	163.0	9.891000e+04	0.433354	1.647963e-03
Africa	11230524.0	5.818190e+08	248668.0	1.373486e+09	0.423607	1.810487e-04
Jordan	1638338.0	4.260392e+06	13849.0	1.026902e+07	0.414878	1.348619e-03
Pakistan	1511754.0	8.561347e+07	30237.0	2.251999e+08	0.380167	1.342674e-04
Georgia	1616159.0	1.413257e+06	16231.0	3.979773e+06	0.355110	4.078373e-03
Moldova	502956.0	1.305637e+06	10657.0	4.024025e+06	0.324460	2.648343e-03
Nepal	977200.0	9.492136e+06	11941.0	2.967492e+07	0.319871	4.023937e-04
Palestine	648039.0	1.655605e+06	5532.0	5.222756e+06	0.316998	1.059211e-03
Kyrgyzstan	200556.0	2.076513e+06	3403.0	6.628347e+06	0.313278	5.134010e-04
Belize	56816.0	1.226920e+05	654.0	4.049150e+05	0.303007	1.615154e-03
Guernsey	0.0	1.593100e+04	0.0	6.338500e+04	0.251337	0.000000e+00
Laos	143240.0	1.802450e+06	623.0	7.379358e+06	0.244256	8.442469e-05
South Africa	3659100.0	1.442119e+07	99135.0	6.004200e+07	0.240185	1.651094e-03
Cayman Islands	19373.0	1.533900e+04	17.0	6.649800e+04	0.230669	2.556468e-04
Finland	658559.0	1.235072e+06	2381.0	5.548361e+06	0.222601	4.291357e-04
Low income	1805056.0	1.391686e+08	41804.0	6.651490e+08	0.209229	6.284907e-05
Kenya	323002.0	1.141772e+07	5640.0	5.498570e+07	0.207649	1.025721e-04
Iran	7066975.0	1.673545e+07	137267.0	8.502876e+07	0.196821	1.614360e-03
Ethiopia	468786.0	2.131937e+07	7467.0	1.178762e+08	0.180862	6.334611e-05
Jamaica	128079.0	4.916840e+05	2814.0	2.973462e+06	0.165357	9.463716e-04
Saint Lucia	22729.0	2.625500e+04	360.0	1.844010e+05	0.142380	1.952267e-03
Saint Vincent and the Grenadines	10045.0	1.569000e+04	106.0	1.112690e+05	0.141010	9.526463e-04
Saint Kitts and Nevis	5530.0	6.553000e+03	42.0	5.354600e+04	0.122381	7.843723e-04
Guinea	36397.0	1.586407e+06	440.0	1.349724e+07	0.117536	3.259926e-05
Guyana	62986.0	8.656300e+04	1222.0	7.903290e+05	0.109528	1.546192e-03
Eswatini	69211.0	1.107650e+05	1390.0	1.172369e+06	0.094480	1.185634e-03
Anguilla	2555.0	1.421000e+03	10.0	1.512500e+04	0.093950	6.611570e-04
Namibia	157275.0	2.347090e+05	4010.0	2.587344e+06	0.090714	1.549852e-03
Dominica	11142.0	6.417000e+03	57.0	7.217200e+04	0.088913	7.897800e-04
Cote d'Ivoire	81552.0	2.285727e+06	794.0	2.705363e+07	0.084489	2.934911e-05
Antigua and Barbuda	7451.0	8.107000e+03	135.0	9.872800e+04	0.082114	1.367393e-03
Zambia	313203.0	1.321156e+06	3955.0	1.892066e+07	0.069826	2.090308e-04
Rwanda	129533.0	9.206440e+05	1457.0	1.327652e+07	0.069344	1.097426e-04
Malawi	85362.0	1.267776e+06	2617.0	1.964768e+07	0.064525	1.331964e-04
Andorra	38249.0	4.802000e+03	151.0	7.735400e+04	0.062078	1.952065e-03
Fiji	63999.0	2.829500e+04	836.0	9.028990e+05	0.031338	9.259064e-04
Bosnia and Herzegovina	371553.0	9.386200e+04	15506.0	3.263459e+06	0.028762	4.751400e-03
Mauritius	71004.0	3.515300e+04	786.0	1.273428e+06	0.027605	6.172316e-04
Bahamas	33198.0	9.258000e+03	771.0	3.969140e+05	0.023325	1.942486e-03
Honduras	412753.0	2.290260e+05	10779.0	1.006299e+07	0.022759	1.071152e-03
Libya	495972.0	1.416880e+05	6279.0	6.958538e+06	0.020362	9.023447e-04
Senegal	85712.0	3.485540e+05	1960.0	1.719631e+07	0.020269	1.139780e-04
Egypt	486381.0	2.078777e+06	24132.0	1.042583e+08	0.019939	2.314635e-04
Nauru	0.0	1.680000e+02	0.0	1.087300e+04	0.015451	0.000000e+00
Uganda	163447.0	7.200770e+05	3594.0	4.712353e+07	0.015281	7.626763e-05
Nigeria	254598.0	3.160328e+06	3143.0	2.114007e+08	0.014949	1.486750e-05
Cape Verde	55889.0	7.462000e+03	402.0	5.619010e+05	0.013280	7.154285e-04
Tajikistan	17786.0	1.247450e+05	125.0	9.749625e+06	0.012795	1.282101e-05
Oman	383389.0	6.508100e+04	4246.0	5.223376e+06	0.012460	8.128842e-04
Mozambique	225096.0	2.958120e+05	2227.0	3.216304e+07	0.009197	6.924096e-05
Grenada	13707.0	1.037000e+03	216.0	1.130150e+05	0.009176	1.911251e-03
Kuwait	620980.0	3.092700e+04	2540.0	4.328553e+06	0.007145	5.868012e-04
Timor	22732.0	7.881000e+03	129.0	1.343875e+06	0.005864	9.599107e-05
Ghana	159891.0	1.809500e+05	1442.0	3.173213e+07	0.005702	4.544290e-05
Mauritania	58638.0	2.465900e+04	979.0	4.775110e+06	0.005164	2.050215e-04
Sweden	2451464.0	4.761600e+04	17611.0	1.016016e+07	0.004687	1.733339e-03
Botswana	263950.0	1.043500e+04	2619.0	2.397240e+06	0.004353	1.092506e-03
Algeria	265079.0	1.707860e+05	6843.0	4.461663e+07	0.003828	1.533733e-04
Haiti	30350.0	4.145300e+04	897.0	1.154168e+07	0.003592	7.771830e-05
French Polynesia	67660.0	8.550000e+02	641.0	2.825340e+05	0.003026	2.268753e-03
Central African Republic	14225.0	1.288700e+04	113.0	4.919987e+06	0.002619	2.296754e-05
Somalia	26351.0	3.729200e+04	1349.0	1.635950e+07	0.002280	8.245973e-05
Liberia	7510.0	1.008900e+04	294.0	5.180208e+06	0.001948	5.675448e-05
Sudan	61525.0	4.557200e+04	3910.0	4.490935e+07	0.001015	8.706427e-05
Sierra Leone	7665.0	7.981000e+03	125.0	8.141343e+06	0.000980	1.535373e-05
Guinea-Bissau	8027.0	1.658000e+03	167.0	2.015490e+06	0.000823	8.285826e-05
South Sudan	16989.0	7.489000e+03	137.0	1.138138e+07	0.000658	1.203721e-05
Gabon	47543.0	1.486000e+03	303.0	2.278829e+06	0.000652	1.329630e-04
Madagascar	63666.0	1.354400e+04	1366.0	2.842733e+07	0.000476	4.805234e-05
Democratic Republic of Congo	86039.0	4.230300e+04	1335.0	9.237799e+07	0.000458	1.445149e-05
Equatorial Guinea	15885.0	6.390000e+02	183.0	1.449891e+06	0.000441	1.262164e-04
Cameroon	119240.0	1.029000e+04	1923.0	2.722426e+07	0.000378	7.063552e-05
Afghanistan	174081.0	1.374200e+04	7617.0	3.983543e+07	0.000345	1.912117e-04
Congo	24020.0	1.486000e+03	409.0	5.657017e+06	0.000263	7.229959e-05
Iraq	2305083.0	9.985000e+03	25013.0	4.117935e+07	0.000242	6.074161e-04
Gambia	12039.0	4.290000e+02	365.0	2.486937e+06	0.000173	1.467669e-04
Myanmar	592139.0	3.800000e+03	19376.0	5.480601e+07	0.000069	3.535378e-04
Burundi	38127.0	4.600000e+02	38.0	1.225543e+07	0.000038	3.100667e-06
Northern Cyprus	0.0	1.988000e+03	0.0	1.474690e+08	0.000013	0.000000e+00
Chad	7257.0	0.000000e+00	190.0	1.691498e+07	0.000000	1.123264e-05
Angola	98746.0	0.000000e+00	1903.0	3.393361e+07	0.000000	5.608009e-05
Tonga	355.0	0.000000e+00	0.0	1.067590e+05	0.000000	0.000000e+00
Yemen	11772.0	0.000000e+00	2135.0	3.049064e+07	0.000000	7.002149e-05
Armenia	420498.0	0.000000e+00	8495.0	2.968128e+06	0.000000	2.862073e-03
Tokelau	0.0	0.000000e+00	0.0	1.368000e+03	0.000000	0.000000e+00
Togo	36808.0	0.000000e+00	272.0	8.478242e+06	0.000000	3.208212e-05
Cook Islands	2.0	0.000000e+00	0.0	1.757200e+04	0.000000	0.000000e+00
Comoros	8033.0	0.000000e+00	161.0	8.884560e+05	0.000000	1.812133e-04
Austria	2744023.0	0.000000e+00	14902.0	9.043072e+06	0.000000	1.647891e-03
Turkmenistan	0.0	0.000000e+00	0.0	6.117933e+06	0.000000	0.000000e+00
Venezuela	515582.0	0.000000e+00	5645.0	2.870495e+07	0.000000	1.966560e-04
Tuvalu	0.0	0.000000e+00	0.0	1.192500e+04	0.000000	0.000000e+00
Costa Rica	811040.0	0.000000e+00	8057.0	5.139053e+06	0.000000	1.567799e-03
Wallis and Futuna	454.0	0.000000e+00	7.0	1.109400e+04	0.000000	6.309717e-04
Burkina Faso	20751.0	0.000000e+00	375.0	2.149710e+07	0.000000	1.744422e-05
British Virgin Islands	6085.0	0.000000e+00	62.0	3.042300e+04	0.000000	2.037932e-03
Bonaire Sint Eustatius and Saba	7599.0	0.000000e+00	33.0	2.644500e+04	0.000000	1.247873e-03
Bermuda	11561.0	0.000000e+00	123.0	6.209200e+04	0.000000	1.980932e-03
Benin	26776.0	0.000000e+00	163.0	1.245103e+07	0.000000	1.309129e-05
Belarus	923432.0	0.000000e+00	6506.0	9.442867e+06	0.000000	6.889857e-04
Vanuatu	19.0	0.000000e+00	1.0	3.144640e+05	0.000000	3.180014e-06
Vatican	29.0	0.000000e+00	0.0	8.120000e+02	0.000000	0.000000e+00
Turks and Caicos Islands	5868.0	0.000000e+00	36.0	3.922600e+04	0.000000	9.177586e-04
Papua New Guinea	41351.0	0.000000e+00	638.0	9.119005e+06	0.000000	6.996377e-05
Tanzania	33620.0	0.000000e+00	798.0	6.149844e+07	0.000000	1.297594e-05
Djibouti	15547.0	0.000000e+00	189.0	1.002197e+06	0.000000	1.885857e-04
Palau	3823.0	0.000000e+00	6.0	1.817400e+04	0.000000	3.301420e-04
Niue	0.0	0.000000e+00	0.0	1.614000e+03	0.000000	0.000000e+00
Niger	8763.0	0.000000e+00	307.0	2.513081e+07	0.000000	1.221608e-05
Nicaragua	18105.0	0.000000e+00	225.0	6.702379e+06	0.000000	3.357017e-05
New Caledonia	55503.0	0.000000e+00	301.0	2.882170e+05	0.000000	1.044352e-03
Saint Helena	4.0	0.000000e+00	0.0	6.095000e+03	0.000000	0.000000e+00
Netherlands	6540294.0	0.000000e+00	21710.0	1.717309e+07	0.000000	1.264187e-03
Saint Pierre and Miquelon	1089.0	0.000000e+00	1.0	5.771000e+03	0.000000	1.732802e-04
Montserrat	164.0	0.000000e+00	2.0	4.981000e+03	0.000000	4.015258e-04
Samoa	33.0	0.000000e+00	0.0	2.001440e+05	0.000000	0.000000e+00
Monaco	9457.0	0.000000e+00	54.0	3.952000e+04	0.000000	1.366397e-03
Sao Tome and Principe	5934.0	0.000000e+00	76.0	2.233640e+05	0.000000	3.402518e-04
Micronesia (country)	1.0	0.000000e+00	0.0	1.162550e+05	0.000000	0.000000e+00
Marshall Islands	8.0	0.000000e+00	0.0	5.961800e+04	0.000000	0.000000e+00
Mali	30391.0	0.000000e+00	722.0	2.085572e+07	0.000000	3.461879e-05
Lesotho	32612.0	0.000000e+00	696.0	2.159067e+06	0.000000	3.223615e-04
Sint Maarten (Dutch part)	0.0	0.000000e+00	0.0	4.342100e+04	0.000000	0.000000e+00
Slovakia	2150666.0	0.000000e+00	18567.0	5.449270e+06	0.000000	3.407245e-03
Kiribati	2953.0	0.000000e+00	11.0	1.213880e+05	0.000000	9.061851e-05
Solomon Islands	7261.0	0.000000e+00	106.0	7.039950e+05	0.000000	1.505693e-04
Jersey	0.0	0.000000e+00	0.0	1.010730e+05	0.000000	0.000000e+00
International	721.0	0.000000e+00	15.0	1.474690e+08	0.000000	1.017163e-07
Pitcairn	0.0	0.000000e+00	0.0	4.700000e+01	0.000000	0.000000e+00
Eritrea	9705.0	0.000000e+00	103.0	3.601462e+06	0.000000	2.859950e-05
Syria	54744.0	0.000000e+00	3082.0	1.827570e+07	0.000000	1.686392e-04
Falkland Islands	115.0	0.000000e+00	0.0	3.528000e+03	0.000000	0.000000e+00

Now we select the desired countries

selected_countries = ['France', 'Brazil','Estonia','Azerbaijan','Paraguay']

df_p2_q2 = df_p2_q2.loc[selected_countries] 
df_p2_q2

	new_cases	new_vaccinations	new_deaths	population	ratio1	ratio2
location
France	23316777.0	140849628.0	138537.0	67422000.0	2.089075	0.002055
Brazil	28741413.0	390272980.0	650254.0	213993441.0	1.823761	0.003039
Estonia	504148.0	1955946.0	2268.0	1325188.0	1.475976	0.001711
Azerbaijan	787367.0	10231821.0	9454.0	10223344.0	1.000829	0.000925
Paraguay	642573.0	4105501.0	18422.0	7219641.0	0.568657	0.002552

plt.figure(figsize=(12,8))
ax = sns.scatterplot(df_p2_q2['ratio1'], df_p2_q2['ratio2'], alpha=0.8)
ax.set_title("The effect of vaccination on the number of deaths",fontsize=15)
ax.set_xlabel('new_vaccinations / population',fontsize=15)
ax.set_ylabel('new_deaths / population',fontsize=15)
plt.show()

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

According to the above chart, it can be said that increasing the number of vaccinations does not necessarily reduce the number of deaths and parameters other than the number of vaccinations affect the number of deaths.

Question 3

In this part, We intend to examine the speed of vaccination in different countries. To do this, we sort the values in new_vaccinations column, and after sorting, we select one country out of every fifth one for review and comparison.

df_p2_q3 = df_p1_q2.sort_values(by= ['new_vaccinations'], ascending= [False])
df_p2_q3 

	new_cases	new_vaccinations	new_deaths	population
location
World	439011701.0	1.122516e+10	5946817.0	7.874966e+09
Asia	117811418.0	7.544342e+09	1351250.0	4.678445e+09
Upper middle income	118212049.0	4.804681e+09	2421471.0	2.513673e+09
Lower middle income	82588725.0	3.974325e+09	1271503.0	3.330653e+09
China	109423.0	3.061013e+09	4640.0	1.444216e+09
High income	236393139.0	2.306054e+09	2211971.0	1.214930e+09
India	42945160.0	1.708969e+09	507015.0	1.393409e+09
Europe	158815550.0	1.247823e+09	1717056.0	7.489630e+08
North America	93195128.0	1.030269e+09	1374621.0	5.965813e+08
European Union	110033392.0	8.184271e+08	1015772.0	4.471899e+08
South America	54222621.0	7.462801e+08	1247285.0	4.342601e+08
Africa	11230524.0	5.818190e+08	248668.0	1.373486e+09
United States	79143715.0	5.537498e+08	954518.0	3.329151e+08
Brazil	28741413.0	3.902730e+08	650254.0	2.139934e+08
Indonesia	5630096.0	2.724306e+08	149036.0	2.763618e+08
Germany	15175471.0	1.701530e+08	123431.0	8.390047e+07
Japan	5143816.0	1.667069e+08	24140.0	1.260508e+08
Mexico	5521744.0	1.451079e+08	311470.0	1.302622e+08
Russia	16353868.0	1.434654e+08	346197.0	1.459120e+08
Turkey	13382896.0	1.434464e+08	94837.0	8.504274e+07
France	23316777.0	1.408496e+08	138537.0	6.742200e+07
Low income	1805056.0	1.391686e+08	41804.0	6.651490e+08
United Kingdom	18241147.0	1.372498e+08	160727.0	6.820711e+07
Italy	12868066.0	1.343115e+08	155245.0	6.036747e+07
Vietnam	3709481.0	1.209025e+08	40637.0	9.816883e+07
South Korea	3691487.0	1.192514e+08	8394.0	5.130518e+07
Thailand	2958176.0	1.087844e+08	23072.0	6.995084e+07
Bangladesh	1945108.0	8.807564e+07	29053.0	1.663035e+08
Pakistan	1511754.0	8.561347e+07	30237.0	2.251999e+08
Argentina	8912317.0	8.251164e+07	126390.0	4.560582e+07
Canada	3303284.0	8.106477e+07	36728.0	3.806791e+07
Oceania	3735739.0	7.462766e+07	7922.0	4.321995e+07
Malaysia	3496090.0	6.741625e+07	32942.0	3.277620e+07
Spain	11139724.0	6.310748e+07	100778.0	4.674521e+07
Peru	3518721.0	6.113136e+07	211453.0	3.335942e+07
Saudi Arabia	746066.0	5.737721e+07	9002.0	3.534068e+07
Philippines	3663931.0	5.494906e+07	56506.0	1.110469e+08
Australia	3326214.0	5.379783e+07	5319.0	2.578822e+07
Colombia	6067023.0	4.938413e+07	138899.0	5.126584e+07
Chile	3098110.0	4.855777e+07	42624.0	1.921236e+07
Poland	5694767.0	3.742697e+07	111864.0	3.779700e+07
Taiwan	20584.0	3.695129e+07	853.0	2.385501e+07
Cambodia	131004.0	3.467897e+07	3032.0	1.694645e+07
Sri Lanka	647699.0	3.284874e+07	16267.0	2.149731e+07
Ukraine	5040518.0	3.168315e+07	112459.0	4.346682e+07
Ecuador	844764.0	3.131789e+07	22611.0	1.788847e+07
Cuba	1070730.0	3.065243e+07	8497.0	1.131750e+07
Morocco	1161290.0	2.740605e+07	16002.0	3.734479e+07
Belgium	3563841.0	2.504046e+07	30316.0	1.163233e+07
Ethiopia	468786.0	2.131937e+07	7467.0	1.178762e+08
United Arab Emirates	880970.0	2.018434e+07	2301.0	9.991083e+06
Portugal	3282618.0	1.964667e+07	21111.0	1.016792e+07
Greece	2454429.0	1.907120e+07	25972.0	1.037075e+07
Kazakhstan	1450652.0	1.870224e+07	19239.0	1.899496e+07
Israel	3652454.0	1.801561e+07	10245.0	9.291000e+06
Czechia	3602844.0	1.739288e+07	38771.0	1.072455e+07
Iran	7066975.0	1.673545e+07	137267.0	8.502876e+07
Romania	2748777.0	1.665058e+07	63668.0	1.912777e+07
Switzerland	2842412.0	1.556814e+07	13269.0	8.715494e+06
Uzbekistan	236596.0	1.512707e+07	1637.0	3.393576e+07
South Africa	3659100.0	1.442119e+07	99135.0	6.004200e+07
Singapore	767663.0	1.357254e+07	1040.0	5.453600e+06
Guatemala	784024.0	1.341083e+07	17021.0	1.824987e+07
Dominican Republic	575157.0	1.331425e+07	4370.0	1.095371e+07
Denmark	2805858.0	1.317204e+07	4633.0	5.813302e+06
Hong Kong	293754.0	1.302273e+07	1168.0	7.552800e+06
Bolivia	893775.0	1.258651e+07	21443.0	1.183294e+07
Kenya	323002.0	1.141772e+07	5640.0	5.498570e+07
Norway	1279169.0	1.123317e+07	1666.0	5.465629e+06
New Zealand	166103.0	1.067335e+07	56.0	5.126300e+06
Ireland	1315500.0	1.056764e+07	6540.0	4.982904e+06
Azerbaijan	787367.0	1.023182e+07	9454.0	1.022334e+07
Nepal	977200.0	9.492136e+06	11941.0	2.967492e+07
Tunisia	999441.0	8.530717e+06	27824.0	1.193576e+07
Hungary	1793120.0	8.276404e+06	44134.0	9.634162e+06
Uruguay	846889.0	7.622054e+06	7005.0	3.485152e+06
Zimbabwe	237509.0	7.080893e+06	5396.0	1.509217e+07
Panama	756085.0	5.934725e+06	8098.0	4.381583e+06
Qatar	357583.0	5.106175e+06	670.0	2.930524e+06
El Salvador	156456.0	4.886767e+06	4077.0	6.518500e+06
Lebanon	1072537.0	4.755512e+06	10115.0	6.769151e+06
Lithuania	913223.0	4.449324e+06	8442.0	2.689862e+06
Mongolia	909379.0	4.407760e+06	2171.0	3.329282e+06
Jordan	1638338.0	4.260392e+06	13849.0	1.026902e+07
Bulgaria	1096194.0	4.143716e+06	35696.0	6.896655e+06
Paraguay	642573.0	4.105501e+06	18422.0	7.219641e+06
Croatia	1058453.0	3.677004e+06	15122.0	4.081657e+06
Serbia	1916707.0	3.257010e+06	15318.0	6.871547e+06
Nigeria	254598.0	3.160328e+06	3143.0	2.114007e+08
Slovenia	897040.0	2.956720e+06	6327.0	2.078723e+06
Bahrain	519584.0	2.927653e+06	1511.0	1.748295e+06
Latvia	673218.0	2.855540e+06	5284.0	1.866934e+06
Cote d'Ivoire	81552.0	2.285727e+06	794.0	2.705363e+07
Egypt	486381.0	2.078777e+06	24132.0	1.042583e+08
Kyrgyzstan	200556.0	2.076513e+06	3403.0	6.628347e+06
Estonia	504148.0	1.955946e+06	2268.0	1.325188e+06
Laos	143240.0	1.802450e+06	623.0	7.379358e+06
Palestine	648039.0	1.655605e+06	5532.0	5.222756e+06
Guinea	36397.0	1.586407e+06	440.0	1.349724e+07
Trinidad and Tobago	128691.0	1.503347e+06	3637.0	1.403374e+06
Albania	271825.0	1.415150e+06	3474.0	2.872934e+06
Georgia	1616159.0	1.413257e+06	16231.0	3.979773e+06
Kosovo	226392.0	1.330147e+06	3116.0	1.782115e+06
Zambia	313203.0	1.321156e+06	3955.0	1.892066e+07
Moldova	502956.0	1.305637e+06	10657.0	4.024025e+06
Malawi	85362.0	1.267776e+06	2617.0	1.964768e+07
Finland	658559.0	1.235072e+06	2381.0	5.548361e+06
Malta	71497.0	1.205547e+06	608.0	5.161000e+05
Luxembourg	186210.0	1.178840e+06	995.0	6.348140e+05
North Macedonia	298195.0	9.505510e+05	9036.0	2.082661e+06
Rwanda	129533.0	9.206440e+05	1457.0	1.327652e+07
Bhutan	13535.0	8.620330e+05	6.0	7.799000e+05
Maldives	171114.0	7.707100e+05	297.0	5.436200e+05
Uganda	163447.0	7.200770e+05	3594.0	4.712353e+07
Macao	81.0	6.627460e+05	0.0	6.583910e+05
Montenegro	230512.0	5.963530e+05	2683.0	6.280510e+05
Jamaica	128079.0	4.916840e+05	2814.0	2.973462e+06
Cyprus	326611.0	4.282040e+05	859.0	8.960050e+05
Senegal	85712.0	3.485540e+05	1960.0	1.719631e+07
Suriname	78353.0	3.429360e+05	1317.0	5.917980e+05
Mozambique	225096.0	2.958120e+05	2227.0	3.216304e+07
Barbados	55543.0	2.868630e+05	316.0	2.877080e+05
Brunei	71667.0	2.627530e+05	129.0	4.415320e+05
Iceland	135748.0	2.588510e+05	66.0	3.687920e+05
Namibia	157275.0	2.347090e+05	4010.0	2.587344e+06
Honduras	412753.0	2.290260e+05	10779.0	1.006299e+07
Curacao	39016.0	1.840890e+05	261.0	1.647960e+05
Ghana	159891.0	1.809500e+05	1442.0	3.173213e+07
Algeria	265079.0	1.707860e+05	6843.0	4.461663e+07
Libya	495972.0	1.416880e+05	6279.0	6.958538e+06
Tajikistan	17786.0	1.247450e+05	125.0	9.749625e+06
Belize	56816.0	1.226920e+05	654.0	4.049150e+05
Eswatini	69211.0	1.107650e+05	1390.0	1.172369e+06
Aruba	33684.0	1.091530e+05	212.0	1.071950e+05
Bosnia and Herzegovina	371553.0	9.386200e+04	15506.0	3.263459e+06
Isle of Man	23099.0	9.206400e+04	80.0	8.541000e+04
Gibraltar	15632.0	8.906400e+04	101.0	3.369100e+04
Guyana	62986.0	8.656300e+04	1222.0	7.903290e+05
Liechtenstein	12165.0	7.022800e+04	78.0	3.825400e+04
Oman	383389.0	6.508100e+04	4246.0	5.223376e+06
Sweden	2451464.0	4.761600e+04	17611.0	1.016016e+07
Sudan	61525.0	4.557200e+04	3910.0	4.490935e+07
Greenland	11760.0	4.333600e+04	19.0	5.686800e+04
Seychelles	39408.0	4.286300e+04	163.0	9.891000e+04
Democratic Republic of Congo	86039.0	4.230300e+04	1335.0	9.237799e+07
Haiti	30350.0	4.145300e+04	897.0	1.154168e+07
Somalia	26351.0	3.729200e+04	1349.0	1.635950e+07
Mauritius	71004.0	3.515300e+04	786.0	1.273428e+06
Faeroe Islands	34658.0	3.481300e+04	28.0	4.905300e+04
Kuwait	620980.0	3.092700e+04	2540.0	4.328553e+06
Fiji	63999.0	2.829500e+04	836.0	9.028990e+05
Saint Lucia	22729.0	2.625500e+04	360.0	1.844010e+05
Mauritania	58638.0	2.465900e+04	979.0	4.775110e+06
San Marino	14435.0	2.014100e+04	112.0	3.401000e+04
Guernsey	0.0	1.593100e+04	0.0	6.338500e+04
Saint Vincent and the Grenadines	10045.0	1.569000e+04	106.0	1.112690e+05
Cayman Islands	19373.0	1.533900e+04	17.0	6.649800e+04
Afghanistan	174081.0	1.374200e+04	7617.0	3.983543e+07
Madagascar	63666.0	1.354400e+04	1366.0	2.842733e+07
Central African Republic	14225.0	1.288700e+04	113.0	4.919987e+06
Botswana	263950.0	1.043500e+04	2619.0	2.397240e+06
Cameroon	119240.0	1.029000e+04	1923.0	2.722426e+07
Liberia	7510.0	1.008900e+04	294.0	5.180208e+06
Iraq	2305083.0	9.985000e+03	25013.0	4.117935e+07
Bahamas	33198.0	9.258000e+03	771.0	3.969140e+05
Antigua and Barbuda	7451.0	8.107000e+03	135.0	9.872800e+04
Sierra Leone	7665.0	7.981000e+03	125.0	8.141343e+06
Timor	22732.0	7.881000e+03	129.0	1.343875e+06
South Sudan	16989.0	7.489000e+03	137.0	1.138138e+07
Cape Verde	55889.0	7.462000e+03	402.0	5.619010e+05
Saint Kitts and Nevis	5530.0	6.553000e+03	42.0	5.354600e+04
Dominica	11142.0	6.417000e+03	57.0	7.217200e+04
Andorra	38249.0	4.802000e+03	151.0	7.735400e+04
Myanmar	592139.0	3.800000e+03	19376.0	5.480601e+07
Northern Cyprus	0.0	1.988000e+03	0.0	1.474690e+08
Guinea-Bissau	8027.0	1.658000e+03	167.0	2.015490e+06
Congo	24020.0	1.486000e+03	409.0	5.657017e+06
Gabon	47543.0	1.486000e+03	303.0	2.278829e+06
Anguilla	2555.0	1.421000e+03	10.0	1.512500e+04
Grenada	13707.0	1.037000e+03	216.0	1.130150e+05
French Polynesia	67660.0	8.550000e+02	641.0	2.825340e+05
Equatorial Guinea	15885.0	6.390000e+02	183.0	1.449891e+06
Burundi	38127.0	4.600000e+02	38.0	1.225543e+07
Gambia	12039.0	4.290000e+02	365.0	2.486937e+06
Nauru	0.0	1.680000e+02	0.0	1.087300e+04
British Virgin Islands	6085.0	0.000000e+00	62.0	3.042300e+04
Palau	3823.0	0.000000e+00	6.0	1.817400e+04
Burkina Faso	20751.0	0.000000e+00	375.0	2.149710e+07
Tonga	355.0	0.000000e+00	0.0	1.067590e+05
Tokelau	0.0	0.000000e+00	0.0	1.368000e+03
Togo	36808.0	0.000000e+00	272.0	8.478242e+06
Turkmenistan	0.0	0.000000e+00	0.0	6.117933e+06
Turks and Caicos Islands	5868.0	0.000000e+00	36.0	3.922600e+04
Tuvalu	0.0	0.000000e+00	0.0	1.192500e+04
Bonaire Sint Eustatius and Saba	7599.0	0.000000e+00	33.0	2.644500e+04
Bermuda	11561.0	0.000000e+00	123.0	6.209200e+04
Lesotho	32612.0	0.000000e+00	696.0	2.159067e+06
Benin	26776.0	0.000000e+00	163.0	1.245103e+07
Belarus	923432.0	0.000000e+00	6506.0	9.442867e+06
Netherlands	6540294.0	0.000000e+00	21710.0	1.717309e+07
Austria	2744023.0	0.000000e+00	14902.0	9.043072e+06
Armenia	420498.0	0.000000e+00	8495.0	2.968128e+06
Nicaragua	18105.0	0.000000e+00	225.0	6.702379e+06
Vanuatu	19.0	0.000000e+00	1.0	3.144640e+05
Vatican	29.0	0.000000e+00	0.0	8.120000e+02
Venezuela	515582.0	0.000000e+00	5645.0	2.870495e+07
Wallis and Futuna	454.0	0.000000e+00	7.0	1.109400e+04
Angola	98746.0	0.000000e+00	1903.0	3.393361e+07
Yemen	11772.0	0.000000e+00	2135.0	3.049064e+07
Niger	8763.0	0.000000e+00	307.0	2.513081e+07
New Caledonia	55503.0	0.000000e+00	301.0	2.882170e+05
Syria	54744.0	0.000000e+00	3082.0	1.827570e+07
Tanzania	33620.0	0.000000e+00	798.0	6.149844e+07
Papua New Guinea	41351.0	0.000000e+00	638.0	9.119005e+06
Saint Helena	4.0	0.000000e+00	0.0	6.095000e+03
Mali	30391.0	0.000000e+00	722.0	2.085572e+07
Jersey	0.0	0.000000e+00	0.0	1.010730e+05
Saint Pierre and Miquelon	1089.0	0.000000e+00	1.0	5.771000e+03
Marshall Islands	8.0	0.000000e+00	0.0	5.961800e+04
Samoa	33.0	0.000000e+00	0.0	2.001440e+05
International	721.0	0.000000e+00	15.0	1.474690e+08
Sao Tome and Principe	5934.0	0.000000e+00	76.0	2.233640e+05
Micronesia (country)	1.0	0.000000e+00	0.0	1.162550e+05
Sint Maarten (Dutch part)	0.0	0.000000e+00	0.0	4.342100e+04
Slovakia	2150666.0	0.000000e+00	18567.0	5.449270e+06
Falkland Islands	115.0	0.000000e+00	0.0	3.528000e+03
Solomon Islands	7261.0	0.000000e+00	106.0	7.039950e+05
Eritrea	9705.0	0.000000e+00	103.0	3.601462e+06
Pitcairn	0.0	0.000000e+00	0.0	4.700000e+01
Niue	0.0	0.000000e+00	0.0	1.614000e+03
Monaco	9457.0	0.000000e+00	54.0	3.952000e+04
Costa Rica	811040.0	0.000000e+00	8057.0	5.139053e+06
Cook Islands	2.0	0.000000e+00	0.0	1.757200e+04
Comoros	8033.0	0.000000e+00	161.0	8.884560e+05
Chad	7257.0	0.000000e+00	190.0	1.691498e+07
Montserrat	164.0	0.000000e+00	2.0	4.981000e+03
Kiribati	2953.0	0.000000e+00	11.0	1.213880e+05
Djibouti	15547.0	0.000000e+00	189.0	1.002197e+06

Now we select the desired countries

pd.reset_option('display.max_rows')

selected_countries = ['United Kingdom', 'Taiwan', 'Lithuania', 'Brazil', 'Italy']

df_p2_q3 = df[df['location'].isin(selected_countries)]
df_p2_q3 

	iso_code	continent	location	date	total_cases	new_cases	new_cases_smoothed	total_deaths	new_deaths	new_deaths_smoothed	...	male_smokers	handwashing_facilities	hospital_beds_per_thousand	life_expectancy	human_development_index	excess_mortality_cumulative_absolute	excess_mortality_cumulative	excess_mortality	excess_mortality_cumulative_per_million	shamsi_date
21222	BRA	South America	Brazil	2020-02-26	1.0	1.0	0.000	0.0	0.0	0.000	...	17.9	50.790872	2.20	75.88	0.765	-inf	-inf	-inf	-inf	1398-12-07
21223	BRA	South America	Brazil	2020-02-27	1.0	0.0	0.000	0.0	0.0	0.000	...	17.9	50.790872	2.20	75.88	0.765	-inf	-inf	-inf	-inf	1398-12-08
21224	BRA	South America	Brazil	2020-02-28	1.0	0.0	0.000	0.0	0.0	0.000	...	17.9	50.790872	2.20	75.88	0.765	-inf	-inf	-inf	-inf	1398-12-09
21225	BRA	South America	Brazil	2020-02-29	2.0	1.0	0.000	0.0	0.0	0.000	...	17.9	50.790872	2.20	75.88	0.765	7049.6	3.31	5.25	32.943066	1398-12-10
21226	BRA	South America	Brazil	2020-03-01	2.0	0.0	0.000	0.0	0.0	0.000	...	17.9	50.790872	2.20	75.88	0.765	-inf	-inf	-inf	-inf	1398-12-11
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
156566	GBR	Europe	United Kingdom	2022-02-26	18854972.0	0.0	31611.143	161367.0	0.0	103.000	...	24.7	50.790872	2.54	81.32	0.932	-inf	-inf	-inf	-inf	1400-12-07
156567	GBR	Europe	United Kingdom	2022-02-27	18854972.0	0.0	27181.143	161367.0	0.0	92.429	...	24.7	50.790872	2.54	81.32	0.932	-inf	-inf	-inf	-inf	1400-12-08
156568	GBR	Europe	United Kingdom	2022-02-28	18937085.0	82113.0	33408.143	161504.0	137.0	107.714	...	24.7	50.790872	2.54	81.32	0.932	-inf	-inf	-inf	-inf	1400-12-09
156569	GBR	Europe	United Kingdom	2022-03-01	19036570.0	99485.0	41712.714	161773.0	269.0	116.857	...	24.7	50.790872	2.54	81.32	0.932	-inf	-inf	-inf	-inf	1400-12-10
156570	GBR	Europe	United Kingdom	2022-03-02	19081606.0	45036.0	42455.286	161848.0	75.0	104.000	...	24.7	50.790872	2.54	81.32	0.932	-inf	-inf	-inf	-inf	1400-12-11

3770 rows × 68 columns

First we aggregate country data by gregorian_month in 2021 year, then we will use the scatter plot diagram to check the rate of vaccination.

df_p2_q3['date'] = pd.to_datetime(df_p2_q3['date'])
df_p2_q3 = df_p2_q3 [df_p2_q3 ['date'].dt.year == 2021]
df_p2_q3['gregorian_month'] = df_p2_q3['date'].dt.month

df_p2_q3 = df_p2_q3.groupby(by=['location', "gregorian_month"]).sum()

df_p2_q3.reset_index().pivot('gregorian_month','location','new_vaccinations').plot(title='Vaccination rate', grid=True, figsize=(20, 10),fontsize=15)

/tmp/ipykernel_4500/4185576851.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_p2_q3['date'] = pd.to_datetime(df_p2_q3['date'])
/tmp/ipykernel_4500/4185576851.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df_p2_q3['gregorian_month'] = df_p2_q3['date'].dt.month

<AxesSubplot:title={'center':'Vaccination rate'}, xlabel='gregorian_month'>

European countries with smaller populations have had almost the same rate of vaccination rates, but countries with larger populations have had their vaccination rates increase over a period of time, but with the advent of new forms of the coronavirus, their vaccination rates have decreased. Less developed countries also have almost constant vaccination rates.

Question 4

In this section, we examine the strictness trend in the field of corona in Iran. To do this, we use the stringency_index factor.

plt.figure(figsize=(12,8))
ax = sns.scatterplot(df_p1_q7.index, df_p1_q7['stringency_index'], alpha=0.8,)
ax.set_title("Strictness trend in the field of corona in Iran",fontsize=15)
ax.set_xlabel('shamsi month',fontsize=15)
ax.set_ylabel('stringency_index',fontsize=15)
plt.show()

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

Before and after Nowruz, we see the most astrictness, but after a while, the strictness decreases, and then with the arrival of new strains from the corona, we see an increase in strictness.

Question 5

In this part, we use box function from plotly.express library to draw a Box Plot to characterize the number of death in each country

fig = px.box(df_p1_q2, y='new_deaths')
fig.update_layout(autosize=False, width=800, height=30000,)
fig.show()

pd.set_option('display.max_rows',None)

df_p2_q5 = df_p1_q2.sort_values(by= ['new_deaths'], ascending= [False])
df_p2_q5

	new_cases	new_vaccinations	new_deaths	population
location
World	439011701.0	1.122516e+10	5946817.0	7.874966e+09
Upper middle income	118212049.0	4.804681e+09	2421471.0	2.513673e+09
High income	236393139.0	2.306054e+09	2211971.0	1.214930e+09
Europe	158815550.0	1.247823e+09	1717056.0	7.489630e+08
North America	93195128.0	1.030269e+09	1374621.0	5.965813e+08
Asia	117811418.0	7.544342e+09	1351250.0	4.678445e+09
Lower middle income	82588725.0	3.974325e+09	1271503.0	3.330653e+09
South America	54222621.0	7.462801e+08	1247285.0	4.342601e+08
European Union	110033392.0	8.184271e+08	1015772.0	4.471899e+08
United States	79143715.0	5.537498e+08	954518.0	3.329151e+08
Brazil	28741413.0	3.902730e+08	650254.0	2.139934e+08
India	42945160.0	1.708969e+09	507015.0	1.393409e+09
Russia	16353868.0	1.434654e+08	346197.0	1.459120e+08
Mexico	5521744.0	1.451079e+08	311470.0	1.302622e+08
Africa	11230524.0	5.818190e+08	248668.0	1.373486e+09
Peru	3518721.0	6.113136e+07	211453.0	3.335942e+07
United Kingdom	18241147.0	1.372498e+08	160727.0	6.820711e+07
Italy	12868066.0	1.343115e+08	155245.0	6.036747e+07
Indonesia	5630096.0	2.724306e+08	149036.0	2.763618e+08
Colombia	6067023.0	4.938413e+07	138899.0	5.126584e+07
France	23316777.0	1.408496e+08	138537.0	6.742200e+07
Iran	7066975.0	1.673545e+07	137267.0	8.502876e+07
Argentina	8912317.0	8.251164e+07	126390.0	4.560582e+07
Germany	15175471.0	1.701530e+08	123431.0	8.390047e+07
Ukraine	5040518.0	3.168315e+07	112459.0	4.346682e+07
Poland	5694767.0	3.742697e+07	111864.0	3.779700e+07
Spain	11139724.0	6.310748e+07	100778.0	4.674521e+07
South Africa	3659100.0	1.442119e+07	99135.0	6.004200e+07
Turkey	13382896.0	1.434464e+08	94837.0	8.504274e+07
Romania	2748777.0	1.665058e+07	63668.0	1.912777e+07
Philippines	3663931.0	5.494906e+07	56506.0	1.110469e+08
Hungary	1793120.0	8.276404e+06	44134.0	9.634162e+06
Chile	3098110.0	4.855777e+07	42624.0	1.921236e+07
Low income	1805056.0	1.391686e+08	41804.0	6.651490e+08
Vietnam	3709481.0	1.209025e+08	40637.0	9.816883e+07
Czechia	3602844.0	1.739288e+07	38771.0	1.072455e+07
Canada	3303284.0	8.106477e+07	36728.0	3.806791e+07
Bulgaria	1096194.0	4.143716e+06	35696.0	6.896655e+06
Malaysia	3496090.0	6.741625e+07	32942.0	3.277620e+07
Belgium	3563841.0	2.504046e+07	30316.0	1.163233e+07
Pakistan	1511754.0	8.561347e+07	30237.0	2.251999e+08
Bangladesh	1945108.0	8.807564e+07	29053.0	1.663035e+08
Tunisia	999441.0	8.530717e+06	27824.0	1.193576e+07
Greece	2454429.0	1.907120e+07	25972.0	1.037075e+07
Iraq	2305083.0	9.985000e+03	25013.0	4.117935e+07
Japan	5143816.0	1.667069e+08	24140.0	1.260508e+08
Egypt	486381.0	2.078777e+06	24132.0	1.042583e+08
Thailand	2958176.0	1.087844e+08	23072.0	6.995084e+07
Ecuador	844764.0	3.131789e+07	22611.0	1.788847e+07
Netherlands	6540294.0	0.000000e+00	21710.0	1.717309e+07
Bolivia	893775.0	1.258651e+07	21443.0	1.183294e+07
Portugal	3282618.0	1.964667e+07	21111.0	1.016792e+07
Myanmar	592139.0	3.800000e+03	19376.0	5.480601e+07
Kazakhstan	1450652.0	1.870224e+07	19239.0	1.899496e+07
Slovakia	2150666.0	0.000000e+00	18567.0	5.449270e+06
Paraguay	642573.0	4.105501e+06	18422.0	7.219641e+06
Sweden	2451464.0	4.761600e+04	17611.0	1.016016e+07
Guatemala	784024.0	1.341083e+07	17021.0	1.824987e+07
Sri Lanka	647699.0	3.284874e+07	16267.0	2.149731e+07
Georgia	1616159.0	1.413257e+06	16231.0	3.979773e+06
Morocco	1161290.0	2.740605e+07	16002.0	3.734479e+07
Bosnia and Herzegovina	371553.0	9.386200e+04	15506.0	3.263459e+06
Serbia	1916707.0	3.257010e+06	15318.0	6.871547e+06
Croatia	1058453.0	3.677004e+06	15122.0	4.081657e+06
Austria	2744023.0	0.000000e+00	14902.0	9.043072e+06
Jordan	1638338.0	4.260392e+06	13849.0	1.026902e+07
Switzerland	2842412.0	1.556814e+07	13269.0	8.715494e+06
Nepal	977200.0	9.492136e+06	11941.0	2.967492e+07
Honduras	412753.0	2.290260e+05	10779.0	1.006299e+07
Moldova	502956.0	1.305637e+06	10657.0	4.024025e+06
Israel	3652454.0	1.801561e+07	10245.0	9.291000e+06
Lebanon	1072537.0	4.755512e+06	10115.0	6.769151e+06
Azerbaijan	787367.0	1.023182e+07	9454.0	1.022334e+07
North Macedonia	298195.0	9.505510e+05	9036.0	2.082661e+06
Saudi Arabia	746066.0	5.737721e+07	9002.0	3.534068e+07
Cuba	1070730.0	3.065243e+07	8497.0	1.131750e+07
Armenia	420498.0	0.000000e+00	8495.0	2.968128e+06
Lithuania	913223.0	4.449324e+06	8442.0	2.689862e+06
South Korea	3691487.0	1.192514e+08	8394.0	5.130518e+07
Panama	756085.0	5.934725e+06	8098.0	4.381583e+06
Costa Rica	811040.0	0.000000e+00	8057.0	5.139053e+06
Oceania	3735739.0	7.462766e+07	7922.0	4.321995e+07
Afghanistan	174081.0	1.374200e+04	7617.0	3.983543e+07
Ethiopia	468786.0	2.131937e+07	7467.0	1.178762e+08
Uruguay	846889.0	7.622054e+06	7005.0	3.485152e+06
Algeria	265079.0	1.707860e+05	6843.0	4.461663e+07
Ireland	1315500.0	1.056764e+07	6540.0	4.982904e+06
Belarus	923432.0	0.000000e+00	6506.0	9.442867e+06
Slovenia	897040.0	2.956720e+06	6327.0	2.078723e+06
Libya	495972.0	1.416880e+05	6279.0	6.958538e+06
Venezuela	515582.0	0.000000e+00	5645.0	2.870495e+07
Kenya	323002.0	1.141772e+07	5640.0	5.498570e+07
Palestine	648039.0	1.655605e+06	5532.0	5.222756e+06
Zimbabwe	237509.0	7.080893e+06	5396.0	1.509217e+07
Australia	3326214.0	5.379783e+07	5319.0	2.578822e+07
Latvia	673218.0	2.855540e+06	5284.0	1.866934e+06
China	109423.0	3.061013e+09	4640.0	1.444216e+09
Denmark	2805858.0	1.317204e+07	4633.0	5.813302e+06
Dominican Republic	575157.0	1.331425e+07	4370.0	1.095371e+07
Oman	383389.0	6.508100e+04	4246.0	5.223376e+06
El Salvador	156456.0	4.886767e+06	4077.0	6.518500e+06
Namibia	157275.0	2.347090e+05	4010.0	2.587344e+06
Zambia	313203.0	1.321156e+06	3955.0	1.892066e+07
Sudan	61525.0	4.557200e+04	3910.0	4.490935e+07
Trinidad and Tobago	128691.0	1.503347e+06	3637.0	1.403374e+06
Uganda	163447.0	7.200770e+05	3594.0	4.712353e+07
Albania	271825.0	1.415150e+06	3474.0	2.872934e+06
Kyrgyzstan	200556.0	2.076513e+06	3403.0	6.628347e+06
Nigeria	254598.0	3.160328e+06	3143.0	2.114007e+08
Kosovo	226392.0	1.330147e+06	3116.0	1.782115e+06
Syria	54744.0	0.000000e+00	3082.0	1.827570e+07
Cambodia	131004.0	3.467897e+07	3032.0	1.694645e+07
Jamaica	128079.0	4.916840e+05	2814.0	2.973462e+06
Montenegro	230512.0	5.963530e+05	2683.0	6.280510e+05
Botswana	263950.0	1.043500e+04	2619.0	2.397240e+06
Malawi	85362.0	1.267776e+06	2617.0	1.964768e+07
Kuwait	620980.0	3.092700e+04	2540.0	4.328553e+06
Finland	658559.0	1.235072e+06	2381.0	5.548361e+06
United Arab Emirates	880970.0	2.018434e+07	2301.0	9.991083e+06
Estonia	504148.0	1.955946e+06	2268.0	1.325188e+06
Mozambique	225096.0	2.958120e+05	2227.0	3.216304e+07
Mongolia	909379.0	4.407760e+06	2171.0	3.329282e+06
Yemen	11772.0	0.000000e+00	2135.0	3.049064e+07
Senegal	85712.0	3.485540e+05	1960.0	1.719631e+07
Cameroon	119240.0	1.029000e+04	1923.0	2.722426e+07
Angola	98746.0	0.000000e+00	1903.0	3.393361e+07
Norway	1279169.0	1.123317e+07	1666.0	5.465629e+06
Uzbekistan	236596.0	1.512707e+07	1637.0	3.393576e+07
Bahrain	519584.0	2.927653e+06	1511.0	1.748295e+06
Rwanda	129533.0	9.206440e+05	1457.0	1.327652e+07
Ghana	159891.0	1.809500e+05	1442.0	3.173213e+07
Eswatini	69211.0	1.107650e+05	1390.0	1.172369e+06
Madagascar	63666.0	1.354400e+04	1366.0	2.842733e+07
Somalia	26351.0	3.729200e+04	1349.0	1.635950e+07
Democratic Republic of Congo	86039.0	4.230300e+04	1335.0	9.237799e+07
Suriname	78353.0	3.429360e+05	1317.0	5.917980e+05
Guyana	62986.0	8.656300e+04	1222.0	7.903290e+05
Hong Kong	293754.0	1.302273e+07	1168.0	7.552800e+06
Singapore	767663.0	1.357254e+07	1040.0	5.453600e+06
Luxembourg	186210.0	1.178840e+06	995.0	6.348140e+05
Mauritania	58638.0	2.465900e+04	979.0	4.775110e+06
Haiti	30350.0	4.145300e+04	897.0	1.154168e+07
Cyprus	326611.0	4.282040e+05	859.0	8.960050e+05
Taiwan	20584.0	3.695129e+07	853.0	2.385501e+07
Fiji	63999.0	2.829500e+04	836.0	9.028990e+05
Tanzania	33620.0	0.000000e+00	798.0	6.149844e+07
Cote d'Ivoire	81552.0	2.285727e+06	794.0	2.705363e+07
Mauritius	71004.0	3.515300e+04	786.0	1.273428e+06
Bahamas	33198.0	9.258000e+03	771.0	3.969140e+05
Mali	30391.0	0.000000e+00	722.0	2.085572e+07
Lesotho	32612.0	0.000000e+00	696.0	2.159067e+06
Qatar	357583.0	5.106175e+06	670.0	2.930524e+06
Belize	56816.0	1.226920e+05	654.0	4.049150e+05
French Polynesia	67660.0	8.550000e+02	641.0	2.825340e+05
Papua New Guinea	41351.0	0.000000e+00	638.0	9.119005e+06
Laos	143240.0	1.802450e+06	623.0	7.379358e+06
Malta	71497.0	1.205547e+06	608.0	5.161000e+05
Guinea	36397.0	1.586407e+06	440.0	1.349724e+07
Congo	24020.0	1.486000e+03	409.0	5.657017e+06
Cape Verde	55889.0	7.462000e+03	402.0	5.619010e+05
Burkina Faso	20751.0	0.000000e+00	375.0	2.149710e+07
Gambia	12039.0	4.290000e+02	365.0	2.486937e+06
Saint Lucia	22729.0	2.625500e+04	360.0	1.844010e+05
Barbados	55543.0	2.868630e+05	316.0	2.877080e+05
Niger	8763.0	0.000000e+00	307.0	2.513081e+07
Gabon	47543.0	1.486000e+03	303.0	2.278829e+06
New Caledonia	55503.0	0.000000e+00	301.0	2.882170e+05
Maldives	171114.0	7.707100e+05	297.0	5.436200e+05
Liberia	7510.0	1.008900e+04	294.0	5.180208e+06
Togo	36808.0	0.000000e+00	272.0	8.478242e+06
Curacao	39016.0	1.840890e+05	261.0	1.647960e+05
Nicaragua	18105.0	0.000000e+00	225.0	6.702379e+06
Grenada	13707.0	1.037000e+03	216.0	1.130150e+05
Aruba	33684.0	1.091530e+05	212.0	1.071950e+05
Chad	7257.0	0.000000e+00	190.0	1.691498e+07
Djibouti	15547.0	0.000000e+00	189.0	1.002197e+06
Equatorial Guinea	15885.0	6.390000e+02	183.0	1.449891e+06
Guinea-Bissau	8027.0	1.658000e+03	167.0	2.015490e+06
Benin	26776.0	0.000000e+00	163.0	1.245103e+07
Seychelles	39408.0	4.286300e+04	163.0	9.891000e+04
Comoros	8033.0	0.000000e+00	161.0	8.884560e+05
Andorra	38249.0	4.802000e+03	151.0	7.735400e+04
South Sudan	16989.0	7.489000e+03	137.0	1.138138e+07
Antigua and Barbuda	7451.0	8.107000e+03	135.0	9.872800e+04
Timor	22732.0	7.881000e+03	129.0	1.343875e+06
Brunei	71667.0	2.627530e+05	129.0	4.415320e+05
Sierra Leone	7665.0	7.981000e+03	125.0	8.141343e+06
Tajikistan	17786.0	1.247450e+05	125.0	9.749625e+06
Bermuda	11561.0	0.000000e+00	123.0	6.209200e+04
Central African Republic	14225.0	1.288700e+04	113.0	4.919987e+06
San Marino	14435.0	2.014100e+04	112.0	3.401000e+04
Solomon Islands	7261.0	0.000000e+00	106.0	7.039950e+05
Saint Vincent and the Grenadines	10045.0	1.569000e+04	106.0	1.112690e+05
Eritrea	9705.0	0.000000e+00	103.0	3.601462e+06
Gibraltar	15632.0	8.906400e+04	101.0	3.369100e+04
Isle of Man	23099.0	9.206400e+04	80.0	8.541000e+04
Liechtenstein	12165.0	7.022800e+04	78.0	3.825400e+04
Sao Tome and Principe	5934.0	0.000000e+00	76.0	2.233640e+05
Iceland	135748.0	2.588510e+05	66.0	3.687920e+05
British Virgin Islands	6085.0	0.000000e+00	62.0	3.042300e+04
Dominica	11142.0	6.417000e+03	57.0	7.217200e+04
New Zealand	166103.0	1.067335e+07	56.0	5.126300e+06
Monaco	9457.0	0.000000e+00	54.0	3.952000e+04
Saint Kitts and Nevis	5530.0	6.553000e+03	42.0	5.354600e+04
Burundi	38127.0	4.600000e+02	38.0	1.225543e+07
Turks and Caicos Islands	5868.0	0.000000e+00	36.0	3.922600e+04
Bonaire Sint Eustatius and Saba	7599.0	0.000000e+00	33.0	2.644500e+04
Faeroe Islands	34658.0	3.481300e+04	28.0	4.905300e+04
Greenland	11760.0	4.333600e+04	19.0	5.686800e+04
Cayman Islands	19373.0	1.533900e+04	17.0	6.649800e+04
International	721.0	0.000000e+00	15.0	1.474690e+08
Kiribati	2953.0	0.000000e+00	11.0	1.213880e+05
Anguilla	2555.0	1.421000e+03	10.0	1.512500e+04
Wallis and Futuna	454.0	0.000000e+00	7.0	1.109400e+04
Palau	3823.0	0.000000e+00	6.0	1.817400e+04
Bhutan	13535.0	8.620330e+05	6.0	7.799000e+05
Montserrat	164.0	0.000000e+00	2.0	4.981000e+03
Vanuatu	19.0	0.000000e+00	1.0	3.144640e+05
Saint Pierre and Miquelon	1089.0	0.000000e+00	1.0	5.771000e+03
Northern Cyprus	0.0	1.988000e+03	0.0	1.474690e+08
Guernsey	0.0	1.593100e+04	0.0	6.338500e+04
Cook Islands	2.0	0.000000e+00	0.0	1.757200e+04
Vatican	29.0	0.000000e+00	0.0	8.120000e+02
Micronesia (country)	1.0	0.000000e+00	0.0	1.162550e+05
Jersey	0.0	0.000000e+00	0.0	1.010730e+05
Tuvalu	0.0	0.000000e+00	0.0	1.192500e+04
Turkmenistan	0.0	0.000000e+00	0.0	6.117933e+06
Nauru	0.0	1.680000e+02	0.0	1.087300e+04
Pitcairn	0.0	0.000000e+00	0.0	4.700000e+01
Tonga	355.0	0.000000e+00	0.0	1.067590e+05
Tokelau	0.0	0.000000e+00	0.0	1.368000e+03
Falkland Islands	115.0	0.000000e+00	0.0	3.528000e+03
Saint Helena	4.0	0.000000e+00	0.0	6.095000e+03
Macao	81.0	6.627460e+05	0.0	6.583910e+05
Samoa	33.0	0.000000e+00	0.0	2.001440e+05
Sint Maarten (Dutch part)	0.0	0.000000e+00	0.0	4.342100e+04
Marshall Islands	8.0	0.000000e+00	0.0	5.961800e+04
Niue	0.0	0.000000e+00	0.0	1.614000e+03

Based on the box plot, South America, United States ,Brazil ,India ,Russia ,Mexico ,Africa and Peru are outlying countries and we remove them from our data for analysis

pd.reset_option('display.max_rows')

print("median: ", 2284.5)
print("average:", df_p1_q2['new_deaths'].mean())

median:  2284.5
average: 104214.1050420168

According to the median and average values, the skewness of the boxplot is downwards

Question 6

In this section, we use scatterplot to analysis features impact

df_p2_q6 = df[["location", "new_cases", "new_deaths"]]
df_p2_q6 = df_p2_q6.groupby(by="location").sum()

df_p2_q6_p = df[["location", "population_density", "median_age", "handwashing_facilities", 
                 "hospital_beds_per_thousand", "human_development_index"]]
df_p2_q6_p = df_p2_q6_p.groupby(by="location").max()

df_p2_q6 = df_p2_q6.join(df_p2_q6_p)
df_p2_q6

	new_cases	new_deaths	population_density	median_age	handwashing_facilities	hospital_beds_per_thousand	human_development_index
location
Afghanistan	174081.0	7617.0	54.422000	18.600000	37.746000	0.500000	0.511000
Africa	11230524.0	248668.0	464.408404	30.568558	50.790872	3.027939	0.725595
Albania	271825.0	3474.0	104.871000	38.000000	50.790872	2.890000	0.795000
Algeria	265079.0	6843.0	17.348000	29.100000	83.741000	1.900000	0.748000
Andorra	38249.0	151.0	163.755000	30.568558	50.790872	3.027939	0.868000
...	...	...	...	...	...	...	...
Wallis and Futuna	454.0	7.0	464.408404	30.568558	50.790872	3.027939	0.725595
World	439011701.0	5946817.0	58.045000	30.900000	60.130000	2.705000	0.737000
Yemen	11772.0	2135.0	53.508000	20.300000	49.542000	0.700000	0.470000
Zambia	313203.0	3955.0	22.995000	17.700000	13.938000	2.000000	0.584000
Zimbabwe	237509.0	5396.0	42.729000	19.600000	36.791000	1.700000	0.571000

238 rows × 7 columns

for i in ["new_deaths", "new_cases"]:
    for j in ["population_density", "median_age", "handwashing_facilities", 
                 "hospital_beds_per_thousand", "human_development_index"]:
        plt.figure(figsize=(12,8))
        ax = sns.scatterplot(df_p2_q6[j], df_p2_q6[i], alpha=0.8,)
        _title = "The effect of " + j + " on the " + i
        ax.set_title(_title,fontsize=15)
        ax.set_xlabel(j,fontsize=15)
        ax.set_ylabel(i,fontsize=15)
        plt.show()

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

According to the plotted diagrams, the population_density feature and the median_age, handwashing_facilities, hospital_beds_per_thousand, human_development_index features have almost the same effect on the new_deaths, new_cases features.

Question 7

In this section, we examine the relationship between the economic situation of countries and the number of people vaccinated.

df_p2_q7 = df[["location", 'gdp_per_capita']]
df_p2_q7 = df_p2_q7.groupby(by="location").max()
df_p2_q7 = df_p1_q2.join(df_p2_q7)

plt.figure(figsize=(12,8))
ax = sns.scatterplot(df_p2_q7['gdp_per_capita'], df_p2_q7['new_vaccinations'])
ax.set_title("The relationship between the economic status of countries and the number of people vaccinated",fontsize=12)
ax.set_xlabel('gdp_per_capita',fontsize=15)
ax.set_ylabel('new_vaccinations',fontsize=15)

plt.show()

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Countries with a more stable economic situation have more people vaccinated.

Question 8

In this part, we examine the distribution of the number of patients by month in 2021

df['date'] = pd.to_datetime(df['date'])
df_p2_q8 = df[df['date'].dt.year == 2021]
df_p2_q8['gregorian_month'] = df_p2_q8['date'].dt.month

df_p2_q8 = df_p2_q8[['gregorian_month', 'new_cases']]
df_p2_q8 = df_p2_q8.groupby(by="gregorian_month").sum()

plt.figure(figsize=(12,8))
ax = sns.scatterplot(df_p2_q8.index, df_p2_q8['new_cases'], alpha=0.8)
ax.set_title('Distribution of the number of patients by month in 2021',fontsize=15)
ax.set_xlabel('gregorian month',fontsize=15)
ax.set_ylabel('new_cases',fontsize=15)
plt.show()

/tmp/ipykernel_4500/2888721197.py:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/home/mohammad/.local/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

It can almost be said that the distribution of patients by month has a sinusoidal trend and according to the global corona situation such as new forms of corona and the amount of vaccination, the number of patients per month changes and there is no definite trend.

Bonus questions

Question 1

In this part, we show the number of deaths of the last three months in different countries in relation to their population on a map. To do this, we use the geopandas library

df_p3_q1 = df[['location', 'new_deaths', 'population', 'date']]

df_p3_q1 = df_p3_q1.sort_values(by = "date",ascending = True).set_index("date").last("3M")
df_p3_q1 = df_p3_q1.reset_index()

df_p3_q1_p1 = df_p3_q1[["location", "new_deaths"]]
df_p3_q1_p1 = df_p3_q1_p1.groupby(by="location").sum()
df_p3_q1_p2 = df_p3_q1[["location", "population"]]
df_p3_q1_p2 = df_p3_q1_p2.groupby(by="location").max()
df_p3_q1 = df_p3_q1_p1.join(df_p3_q1_p2)

df_p3_q1['deaths_per_population'] = df_p3_q1['new_deaths'] / df_p3_q1['population']
df_p3_q1 = df_p3_q1.drop(['new_deaths', 'population'], axis=1)
df_p3_q1 = df_p3_q1.reset_index()
df_p3_q1

	location	deaths_per_population
0	Afghanistan	0.000007
1	Africa	0.000015
2	Albania	0.000089
3	Algeria	0.000013
4	Andorra	0.000142
...	...	...
227	Wallis and Futuna	0.000000
228	World	0.000067
229	Yemen	0.000005
230	Zambia	0.000012
231	Zimbabwe	0.000026

232 rows × 2 columns

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
world.columns = ['pop_est', 'continent', 'name', 'CODE', 'gdp_md_est', 'geometry']
df_p3_q1 = df_p3_q1.rename(columns = {"location": "name"})
merge = pd.merge(world, df_p3_q1, on = 'name')

merge.plot(column = 'deaths_per_population', figsize = (25, 10), legend = True, cmap ='cubehelix') # cubehelix winter coolwarm
plt.title('The number of deaths of the last 3 months in different countries in relation to their population',fontsize=22)
plt.show()

Question 2

In this section, we add the number of deaths and the number of vaccinated in Iran on a weekly basis. Then, we draw in a suitable diagram.

df_iran['date'] = pd.to_datetime(df_iran['date'])
df_iran['week_number'] = df_iran['date'].dt.week

df_p3_q2_y2020 = df_iran[df_iran['date'].dt.year == 2020]
df_p3_q2_y2020 = df_p3_q2_y2020[['week_number', 'new_deaths', 'new_vaccinations']]
df_p3_q2_y2020 = df_p3_q2_y2020.groupby(by="week_number").sum()

df_p3_q2_y2021 = df_iran[df_iran['date'].dt.year == 2021]
df_p3_q2_y2021 = df_p3_q2_y2021[['week_number', 'new_deaths', 'new_vaccinations']]
df_p3_q2_y2021 = df_p3_q2_y2021.groupby(by="week_number").sum()

df_p3_q2_y2022 = df_iran[df_iran['date'].dt.year == 2022]
df_p3_q2_y2022 = df_p3_q2_y2022[['week_number', 'new_deaths', 'new_vaccinations']]
df_p3_q2_y2022 = df_p3_q2_y2022.groupby(by="week_number").sum()

/tmp/ipykernel_4500/4168250913.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/tmp/ipykernel_4500/4168250913.py:2: FutureWarning:

Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.

/tmp/ipykernel_4500/4168250913.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

df_p3_q2_y2020

	new_deaths	new_vaccinations
week_number
8	8.0	0.0
9	46.0	0.0
10	140.0	0.0
11	530.0	0.0
12	961.0	0.0
13	955.0	0.0
14	963.0	0.0
15	871.0	0.0
16	644.0	0.0
17	592.0	0.0
18	493.0	0.0
19	437.0	0.0
20	348.0	0.0
21	429.0	0.0
22	380.0	0.0
23	484.0	0.0
24	556.0	0.0
25	786.0	0.0
26	885.0	0.0
27	1063.0	0.0
28	1258.0	0.0
29	1359.0	0.0
30	1512.0	0.0
31	1490.0	0.0
32	1237.0	0.0
33	1212.0	0.0
34	1004.0	0.0
35	819.0	0.0
36	831.0	0.0
37	864.0	0.0
38	1144.0	0.0
39	1288.0	0.0
40	1368.0	0.0
41	1587.0	0.0
42	1831.0	0.0
43	2241.0	0.0
44	2682.0	0.0
45	2993.0	0.0
46	3202.0	0.0
47	3309.0	0.0
48	3072.0	0.0
49	2436.0	0.0
50	1886.0	0.0
51	1429.0	0.0
52	1068.0	0.0
53	530.0	0.0

df_p3_q2_y2021

	new_deaths	new_vaccinations
week_number
1	631.0	0.0
2	632.0	0.0
3	580.0	0.0
4	576.0	0.0
5	510.0	0.0
6	476.0	3000.0
7	538.0	0.0
8	590.0	0.0
9	614.0	0.0
10	543.0	0.0
11	567.0	0.0
12	600.0	0.0
13	763.0	0.0
14	1330.0	0.0
15	2242.0	0.0
16	2842.0	71691.0
17	2910.0	129369.0
18	2426.0	0.0
19	2026.0	0.0
20	1661.0	0.0
21	1342.0	0.0
22	1124.0	0.0
23	1035.0	0.0
24	867.0	0.0
25	880.0	0.0
26	947.0	0.0
27	1067.0	0.0
28	1302.0	0.0
29	1639.0	0.0
30	2196.0	0.0
31	3019.0	0.0
32	3813.0	0.0
33	4210.0	0.0
34	4444.0	0.0
35	4192.0	881127.0
36	3637.0	0.0
37	2871.0	1706088.0
38	2178.0	0.0
39	1749.0	0.0
40	1483.0	0.0
41	1483.0	0.0
42	1148.0	0.0
43	1080.0	0.0
44	996.0	0.0
45	868.0	0.0
46	789.0	0.0
47	673.0	171719.0
48	571.0	0.0
49	522.0	0.0
50	361.0	0.0
51	317.0	0.0
52	206.0	0.0
53	317.0	0.0

df_p3_q2_y2022

	new_deaths	new_vaccinations
week_number
1	198.0	0.0
2	197.0	1002481.0
3	155.0	2102200.0
4	194.0	1287747.0
5	406.0	3647420.0
6	888.0	2395218.0
7	1322.0	1281422.0
8	1591.0	1366710.0
9	636.0	689254.0
52	74.0	0.0

df_p3_q2_y2020.plot(y=['new_deaths', 'new_vaccinations'], kind="bar", figsize=(50,30),fontsize=25)
df_p3_q2_y2021.plot(y=['new_deaths', 'new_vaccinations'], kind="bar", figsize=(50,30),fontsize=25)
df_p3_q2_y2022.plot(y=['new_deaths', 'new_vaccinations'], kind="bar", figsize=(50,30),fontsize=25)

<AxesSubplot:xlabel='week_number'>

ax1 = df_p3_q2_y2020.plot(y="new_deaths", kind="bar")
ax1.set_title("new deaths & new vaccinations in different weeks of 2020",fontsize=35)
ax1.set_xlabel('Week Number',fontsize=35)
ax1.set_ylabel('Number',fontsize=35)
df_p3_q2_y2020.plot(y='new_vaccinations', kind="bar", ax=ax1, color="C2", figsize=(50,30),fontsize=25)

ax2 = df_p3_q2_y2021.plot(y="new_deaths", kind="bar")
ax2.set_title("new deaths & new vaccinations in different weeks of 2021",fontsize=35)
ax2.set_xlabel('Week Number',fontsize=35)
ax2.set_ylabel('Number',fontsize=35)
df_p3_q2_y2021.plot(y='new_vaccinations', kind="bar", ax=ax2, color="C2", figsize=(50,30),fontsize=25)

ax3 = df_p3_q2_y2022.plot(y="new_deaths", kind="bar")
ax3.set_title("new deaths & new vaccinations in different weeks of 2022",fontsize=35)
ax3.set_xlabel('Week Number',fontsize=35)
ax3.set_ylabel('Number',fontsize=35)
df_p3_q2_y2022.plot(y='new_vaccinations', kind="bar", ax=ax3, color="C2", figsize=(50,30),fontsize=25)

plt.show()

According to the graphs, it can be concluded that in some special weeks, with the arrival of the new corona wave and due to the low rate of vaccination or non-vaccination, especially in the first year of corona arrival, the number of deaths increased and in other weeks due to inhibition relative coronavirus and increased vaccination, we see fewer deaths.